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You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: A more flexible approach is used by Reiter and Sripada (2002), where users can specify boundary values for attributes like rainfall, specifying, for example, rain counts as moderate above 7 mm/h, as heavy above 20 mm/h, and so on. FOG does not appear to have generic rules governing the use of gradable notions: it does not compute the meaning of a vague term based on the context, but uses fixed boundary values instead. The FOG weather-forecast system, for example, uses numerical input (Rain[Tuesday] = 45 mm) to generate vague output (Heavy rain fell on Tuesday, Goldberg, Driedger, and Kitteridge 1994). Citation Sentence: A more flexible approach is used by Reiter and Sripada ( 2002 ) , where users can specify boundary values for attributes like rainfall , specifying , for example , rain counts as moderate above 7 mm/h , as heavy above 20 mm/h , and so on . Context after the citation: A third approach was implemented in Dial Your Disc (DYD), where the extension of a gradable adjective like famous was computed rather than specified by hand (van Deemter and Odijk 1997). To determine, for example, whether one of Mozart’s piano sonatas could be called a famous sonata, the system looked up the number x of compact disc recordings of this sonata (as listed in an encyclopedia) and compared it to the average number y of CD recordings of each of Mozart’s sonatas. The sonata was called a famous sonata if x >> y. Like DYD, the work reported in this article will abandon the use of fixed boundary values for gradable adjectives, letting these values depend on the context in which the adjective is used. Sometimes we are forced to be vague because the information we have (e.g., based on perception or verbal reports) is itself inexact.

Background

acl_arc_intent_classification:train:1628

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: Furthermore, a number of performance features, largely based on the PARADISE dialogue evaluation scheme (Walker et al., 1997), were automatically logged, derived, or manually annotated. User satisfaction was assessed by asking the subjects to fill out a questionnaire after interacting with each version of the system. Each experiment involved eight users interacting with MIMIC and MIMIC-SI or MIMIC-MI to perform a set of tasks, each requiring the user to obtain specific movie information. Citation Sentence: Furthermore , a number of performance features , largely based on the PARADISE dialogue evaluation scheme ( Walker et al. , 1997 ) , were automatically logged , derived , or manually annotated . Context after the citation: In addition, we logged the cues automatically detected in each user utterance, as well as the initiative distribution for each turn and the dialogue acts selected to generate each system response. The features gathered from the dialogue interactions were analyzed along three dimensions: system performance, discourse features (in terms of characteristics of the resulting dialogues, such as the cues detected in user utterances), and initiative distribution. Our results show that MIMIC's adaptation capabilities 1) led to better system performance in terms of user satisfaction, dialogue efficiency (shorter dialogues), and dialogue quality (fewer ASR timeouts), and 2) better matched user expectations (by giving up task initiative when the user intends to have control of the dialogue interaction) and more efficiently resolved dialogue anomalies (by taking over task initiative to provide guidance when no progress is made in the dialogue, or to constrain user utterances when ASR performance is poor).

Uses

acl_arc_intent_classification:train:1629

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: The system is in the form of an agenda-driven chart-based parser whose foundation is similar to previous formalizations of Chomsky’s Minimalist Program (Stabler, 1997; Harkema, 2000; Niyogi, 2001). My theory of verbal argument structure can be implemented in a unified morpho-syntactic parsing model that interleaves syntactic and semantic parsing. Citation Sentence: The system is in the form of an agenda-driven chart-based parser whose foundation is similar to previous formalizations of Chomsky 's Minimalist Program ( Stabler , 1997 ; Harkema , 2000 ; Niyogi , 2001 ) . Context after the citation: Lexical entries in the system are minimally specified, each consisting of a phonetic form, a list of relevant features, and semantics in the form of a A expression. The basic structure building operation, MERGE, takes two items and creates a larger item. In the process, compatible features are canceled and one of the items projects. Simultaneously, the A expression associated with the licensor is applied to the A expression associated with the licensee (in theoretical linguistic terms, SpellOut).

CompareOrContrast

acl_arc_intent_classification:train:163

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: Shaw and Hatzivassiloglou (1999) propose to generalize the direct evidence method so that it can apply to unseen pairs of adjectives by computing the transitive closure of the ordering relation �. If neither order appears in the training data, then neither a � b nor b � a and an order must be randomly assigned. If the reverse is true, and (b,a) is found more often than (a,b), then b � a. Citation Sentence: Shaw and Hatzivassiloglou ( 1999 ) propose to generalize the direct evidence method so that it can apply to unseen pairs of adjectives by computing the transitive closure of the ordering relation . Context after the citation: That is, if a � c and c � b, we can conclude that a � b. To take an example from the BNC, the adjectives large and green never occur together in the training data, and so would be assigned a random order by the direct evidence method. However, the pairs (large,new) and (new,green) occur fairly frequently. Therefore, in the face of this evidence we can assign this pair the order (large,green), which not coincidently is the correct English word order. The difficulty with applying the transitive closure method to any large dataset is that there often will be evidence for both orders of any given pair.

Background

acl_arc_intent_classification:train:1630

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: We evaluated our translations with IBM's BLEU evaluation metric (Papineni et al., 2001), using the same evaluation method and reference retranslations that were used for evaluation at HLT Workshop 2002 at CLSP (Haji 6 et al., 2002). Citation Sentence: We evaluated our translations with IBM 's BLEU evaluation metric ( Papineni et al. , 2001 ) , using the same evaluation method and reference retranslations that were used for evaluation at HLT Workshop 2002 at CLSP ( Haji 6 et al. , 2002 ) . Context after the citation: We used four reference retranslations of 490 sentences selected from the WSJ sections 22, 23, and 24, which were themselves used as the fifth reference. The evaluation method used is to hold out each reference in turn and evaluate it against the remaining four, averaging the five BLEU scores. Table 3 shows final results of our system compared with GIZA++ and MAGENTA's results. The DBMT with parser I and parser II experiments represent a fully automated translation, while the DBMT experiment on manually annotated trees generates from the Czech tectogrammatical trees prepared by human annotators.

Uses

acl_arc_intent_classification:train:1631

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: Other attempts to address efficiency include the fast Transformation Based Learning (TBL) Toolkit (Ngai and Florian, 2001) which dramatically speeds up training TBL systems, and the translation of TBL rules into finite state machines for very fast tagging (Roche and Schabes, 1997). An example of this is the estimation of maximum entropy models, from simple iterative estimation algorithms used by Ratnaparkhi (1998) that converge very slowly, to complex techniques from the optimisation literature that converge much more rapidly (Malouf, 2002). However, it will be increasingly important as techniques become more complex and corpus sizes grow. Citation Sentence: Other attempts to address efficiency include the fast Transformation Based Learning ( TBL ) Toolkit ( Ngai and Florian , 2001 ) which dramatically speeds up training TBL systems , and the translation of TBL rules into finite state machines for very fast tagging ( Roche and Schabes , 1997 ) . Context after the citation: The TNT POS tagger (Brants, 2000) has also been designed to train and run very quickly, tagging between 30,000 and 60,000 words per second. The Weka package (Witten and Frank, 1999) provides a common framework for several existing machine learning methods including decision trees and support vector machines. This library has been very popular because it allows researchers to experiment with different methods without having to modify code or reformat data. Finally, the Natural Language Toolkit (NLTK) is a package of NLP components implemented in Python (Loper and Bird, 2002).

Background

acl_arc_intent_classification:train:1632

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: PR is closely related to the work of Mann and McCallum (2007, 2008), who concurrently developed the idea of using penalties based on posterior expectations of features to guide semi-supervised learning. Such a statement is data-dependent in the sense that if the model satisfies the constraints then we do not need to change it; if it is far from satisfying it we might need to make very dramatic changes. By contrast the statements we can make in PR are of the form “there should be more short edges than long edges”. Citation Sentence: PR is closely related to the work of Mann and McCallum ( 2007 , 2008 ) , who concurrently developed the idea of using penalties based on posterior expectations of features to guide semi-supervised learning . Context after the citation: They call their method generalized expectation (GE) constraints or alternatively expectation regularization. In the original GE framework, the posteriors of the model on unlabeled data are regularized directly. They train a discriminative model, using conditional likelihood on labeled data and an “expectation regularization” penalty term on the unlabeled data: Notice that there is no intermediate distribution q. For some kinds of constraints this objective is difficult to optimize in 0 and in order to improve efficiency, Bellare, Druck, and McCallum (2009) propose interpreting the PR framework as an approximation to the GE objective in Equation (16).

Background

acl_arc_intent_classification:train:1633

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: The best results on most of our data were obtained using Hidden Naive Bayes (HNB) (Zhang et al., 2005). We experimented with various Weka classifiers, comprising Hidden Naive Bayes, SMO, ID3, LADTree and Decision Table. These two sets of data were used for automatic dialogue act classification, which was run in the Weka system (Witten and Frank, 2005). Citation Sentence: The best results on most of our data were obtained using Hidden Naive Bayes ( HNB ) ( Zhang et al. , 2005 ) . Context after the citation: Therefore, here we show the results of this classifier. Ten-folds crossvalidation was applied throughout. In the first group of experiments we took into consideration all the Yes and No expressions (420 Yes and 46 No) without, however, considering gesture information. The purpose was to see how prosodic information contributes to the classification of dialogue acts.

Uses

acl_arc_intent_classification:train:1634

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: de URL: http://www.sfs.nphil.uni-tuebingen.de/sfb /b4home.html 1 This is, for example, the case for all proposals working with verbal lexical entries that raise the arguments of a verbal complement (Hinrichs and Nakazawa 1989) that also use lexical rules such as the Complement Extraction Lexical Rule (Pollard and Sag 1994) or the Complement Cliticization Lexical Rule (Miller and Sag 1993) to operate on those raised elements. nphil.uni-tuebingen. email: {dm,minnen}@sfs. Citation Sentence: de URL : http://www.sfs.nphil.uni-tuebingen.de/sfb / b4home.html 1 This is , for example , the case for all proposals working with verbal lexical entries that raise the arguments of a verbal complement ( Hinrichs and Nakazawa 1989 ) that also use lexical rules such as the Complement Extraction Lexical Rule ( Pollard and Sag 1994 ) or the Complement Cliticization Lexical Rule ( Miller and Sag 1993 ) to operate on those raised elements . Context after the citation: Also an analysis treating adjunct extraction via lexical rules (van Noord and Bouma 1994) results in an infinite lexicon. Treatments of lexical rules as unary phrase structure rules also require their fully explicit specification, which entails the last problem mentioned above. In addition, computationally treating lexical rules on a par with phrase structure rules fails to take computational advantage of their specific properties. For example, the interaction of lexical rules is explored at run-time, even though the possible interaction can be determined at compile-time given the information available in the lexical rules and the base lexical entries.2 Based on the research results reported in Meurers and Minnen (1995, 1996), we propose a new computational treatment of lexical rules that overcomes these shortcomings and results in a more efficient processing of lexical rules as used in HPSG.

Background

acl_arc_intent_classification:train:1635

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: A number of proposals in the 1990s deliberately limited the extent to which they relied on domain and/or linguistic knowledge and reported promising results in knowledge-poor operational environments (Dagan and Itai 1990, 1991; Lappin and Leass 1994; Nasukawa 1994; Kennedy and Boguraev 1996; Williams, Harvey, and Preston 1996; Baldwin 1997; Mitkov 1996, 1998b). However, the pressing need for the development of robust and inexpensive solutions to meet the demands of practical NLP systems encouraged many researchers to move away from extensive domain and linguistic knowledge and to embark instead upon knowledge-poor anaphora resolution strategies. Much of the earlier work in anaphora resolution heavily exploited domain and linguistic knowledge (Sidner 1979; Carter 1987; Rich and LuperFoy 1988; Carbonell and Brown 1988), which was difficult both to represent and to process, and which required considerable human input. Citation Sentence: A number of proposals in the 1990s deliberately limited the extent to which they relied on domain and/or linguistic knowledge and reported promising results in knowledge-poor operational environments ( Dagan and Itai 1990 , 1991 ; Lappin and Leass 1994 ; Nasukawa 1994 ; Kennedy and Boguraev 1996 ; Williams , Harvey , and Preston 1996 ; Baldwin 1997 ; Mitkov 1996 , 1998b ) . Context after the citation: The drive toward knowledge-poor and robust approaches was further motivated by the emergence of cheaper and more reliable corpus-based NLP tools such as partof-speech taggers and shallow parsers, alongside the increasing availability of corpora and other NLP resources (e.g., ontologies). In fact, the availability of corpora, both raw and annotated with coreferential links, provided a strong impetus to anaphora resolu- tion with regard to both training and evaluation. Corpora (especially when annotated) are an invaluable source not only for empirical research but also for automated learning (e.g., machine learning) methods aiming to develop new rules and approaches; they also provide an important resource for evaluation of the implemented approaches.

Background

acl_arc_intent_classification:train:1636

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: method Context before the citation: ASARES has been previously applied to the acquisition of word pairs sharing semantic relations defined in the Generative Lexicon framework (Pustejovsky, 1995) and called qualia relations (Bouillon et al., 2001). extraction of N-V pairs from the corpus with the inferred patterns. inference of extraction patterns with ASARES; and 3. Citation Sentence: ASARES has been previously applied to the acquisition of word pairs sharing semantic relations defined in the Generative Lexicon framework ( Pustejovsky , 1995 ) and called qualia relations ( Bouillon et al. , 2001 ) . Context after the citation: Here, we propose to use ASARES in a quite similar way to retrieve our valid N-V pairs. However, the N-V combinations sought are more specific than those that were identified in these previous experiments. Formally, ILP aims at inferring logic programs (sets of Horn clauses, noted H) from a set of facts (examples and counter-examples of the concept to be learnt) and background knowledge (B), such that the program H logically entails the examples with respect to the background knowledge and rejects (most of) the counterexamples. This is transcribed by the two logical formulae B n H �= E+, B n H � E−, which set the aim of an ILP algorithm.

Background

acl_arc_intent_classification:train:1637

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: mlSystem ruleFeats + atomFeats We augment mlSystem ruleFeats with more features from our previous work (Markert et al., 2012; Hou et al., 2013a; Hou et al., 2013b) on bridging anaphora recognition and antecedent selection. All rules from the rule-based system are incorporated into mlSystem ruleFeats as the features. mlSystem ruleFeats We provide mlSystem ruleFeats with the same knowledge resources as the rule-based system. Citation Sentence: mlSystem ruleFeats + atomFeats We augment mlSystem ruleFeats with more features from our previous work ( Markert et al. , 2012 ; Hou et al. , 2013a ; Hou et al. , 2013b ) on bridging anaphora recognition and antecedent selection . Context after the citation: Some of these features overlap with the atomic features used in the rule-based system. Table 4 shows all the features we use for recognizing bridging anaphora. “∗” indicates the resources are used in the rule-based system. We apply them to the first element a of a pairwise instance (a, c).

Extends

acl_arc_intent_classification:train:1638

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: The description of the EAGLE workbench for linguistic engineering (Baldwin et al. 1997) mentions a case normalization module that uses a heuristic in which a capitalized word in an ambiguous position should be rewritten without capitalization if it is found lower-cased in the same document. This is similar to “one sense per collocation” idea of Yarowsky (1993). In capitalized-word disambiguation, however, we use this assumption with caution and first apply strategies that rely not just on single words but on words together with their local contexts (n-grams). Citation Sentence: The description of the EAGLE workbench for linguistic engineering ( Baldwin et al. 1997 ) mentions a case normalization module that uses a heuristic in which a capitalized word in an ambiguous position should be rewritten without capitalization if it is found lower-cased in the same document . Context after the citation: This heuristic also employs a database of bigrams and unigrams of lower-cased and capitalized words found in unambiguous positions. It is quite similar to our method for capitalized-word disambiguation. The description of the EAGLE case normalization module provided by Baldwin et al. is, however, very brief and provides no performance evaluation or other details. 12.2 Research in Text Preprocessing

CompareOrContrast

acl_arc_intent_classification:train:1639

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: Each component will return a confidence measure of the reliability of its prediction, c.f. (Elworthy, 1998). For example, the misspelling identifier will specialize in identifying misspellings, the abbreviation component will specialize in identifying abbreviations, etc. unknown word. Citation Sentence: Each component will return a confidence measure of the reliability of its prediction , c.f. ( Elworthy , 1998 ) . Context after the citation: The results from each component are evaluated to determine the final category of the word. There are several advantages to this approach. Firstly, the system can take advantage of existing research. For example, the name recognition module can make use of the considerable research that exists on name recognition, e.g. (McDonald, 1996), (Mani et al., 1996).

Motivation

acl_arc_intent_classification:train:164

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: An exception is Grefenstette et al. (2004), who experimented with determining the political orientation of websites essentially by classifying the concatenation of all the documents found on that site. There has also been work focused upon determining the political leaning (e.g., “liberal” vs. “conservative”) of a document or author, where most previously-proposed methods make no direct use of relationships between the documents to be classified (the “unlabeled” texts) (Laver et al., 2003; Efron, 2004; Mullen and Malouf, 2006). Politically-oriented text Sentiment analysis has specifically been proposed as a key enabling technology in eRulemaking, allowing the automatic analysis of the opinions that people submit (Shulman et al., 2005; Cardie et al., 2006; Kwon et al., 2006). Citation Sentence: An exception is Grefenstette et al. ( 2004 ) , who experimented with determining the political orientation of websites essentially by classifying the concatenation of all the documents found on that site . Context after the citation: Others have applied the NLP technologies of near-duplicate detection and topic-based text categorization to politically oriented text (Yang and Callan, 2005; Purpura and Hillard, 2006). Detecting agreement We used a simple method to learn to identify cross-speaker references indicating agreement. More sophisticated approaches have been proposed (Hillard et al., 2003), including an extension that, in an interesting reversal of our problem, makes use of sentimentpolarity indicators within speech segments (Galley et al., 2004). Also relevant is work on the general problems of dialog-act tagging (Stolcke et al., 2000), citation analysis (Lehnert et al., 1990), and computational rhetorical analysis (Marcu, 2000; Teufel and Moens, 2002).

Background

acl_arc_intent_classification:train:1640

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: Until now, translation models have been evaluated either subjectively (e.g. White and O'Connell 1993) or using relative metrics, such as perplexity with respect to other models (Brown et al. 1993b). 6.1.1 Experiment 1. method is the list of function words in class F. Certainly, more sophisticated word classification methods could produce better models, but even the simple classification in Table 4 should suffice to demonstrate the method's potential. Citation Sentence: Until now , translation models have been evaluated either subjectively ( e.g. White and O'Connell 1993 ) or using relative metrics , such as perplexity with respect to other models ( Brown et al. 1993b ) . Context after the citation: Objective and more accurate tests can be carried out using a "gold standard." I hired bilingual annotators to link roughly 16,000 corresponding words between on-line versions of the Bible in French and English. This bitext was selected to facilitate widespread use and standardization (see Melamed [1998c] for details). The entire Bible bitext comprised 29,614 verse pairs, of which 250 verse pairs were hand-linked using a specially developed annotation tool.

CompareOrContrast

acl_arc_intent_classification:train:1641

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: The three preprocessing steps (tokenization, POS-tagging, lemmatization) are performed using TreeTagger (Schmid, 1995). Due to the special structure of presentations, this corpus will be particularly demanding with respect to the required preprocessing components of an information retrieval system. They cover a wide range of topics from bio genetics to computer science and contain many technical terms. Citation Sentence: The three preprocessing steps ( tokenization , POS-tagging , lemmatization ) are performed using TreeTagger ( Schmid , 1995 ) . Context after the citation: The resulting list of POS-tagged lemmas is weighted using the SMART ‘ltc’8 tf.idf-weighting scheme (Salton, 1989). We implemented a set of filters for word pairs. One group of filters removed unwanted word pairs. Word pairs are filtered if they contain at least one word that a) has less than three letters b) contains only uppercase letters (mostly acronyms) or c) can be found in a stoplist.

Uses

acl_arc_intent_classification:train:1642

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Many statistical parsers (Ratnaparkhi, 1999; Collins, 1999; Charniak, 2001) are based on a history-based probability model (Black et al., 1993), where the probability of each decision in a parse is conditioned on the previous decisions in the parse. Citation Sentence: Many statistical parsers ( Ratnaparkhi , 1999 ; Collins , 1999 ; Charniak , 2001 ) are based on a history-based probability model ( Black et al. , 1993 ) , where the probability of each decision in a parse is conditioned on the previous decisions in the parse . Context after the citation: A major challenge in this approach is choosing a representation of the parse history from which the probability for the next parser decision can be accurately estimated. Previous approaches have used a hand-crafted finite set of features to represent the unbounded parse history (Ratnaparkhi, 1999; Collins, 1999; Charniak, 2001). In the work presented here, we automatically induce a finite set of features to represent the unbounded parse history. We perform this induction using an artificial neural network architecture, called Simple Synchrony Networks (SSNs) (Lane and Henderson, 2001; Henderson, 2000).

Background

acl_arc_intent_classification:train:1643

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: In informal experiments described elsewhere (Melamed 1995), I found that the G2 statistic suggested by Dunning (1993) slightly outperforms 02. For example, Gale and Church (1991, 154) suggest that "02, a x2-like statistic, seems to be a particularly good choice because it makes good use of the off-diagonal cells" in the contingency table. The statistical interdependence between two word types can be estimated more robustly by considering the whole table. Citation Sentence: In informal experiments described elsewhere ( Melamed 1995 ) , I found that the G2 statistic suggested by Dunning ( 1993 ) slightly outperforms 02 . Context after the citation: Let the cells of the contingency table be named as follows: where B(kin,p) = () pk(1 p)n-k are binomial probabilities. The statistic uses maximum likelihood estimates for the probability parameters: p1 = bf P2 — c±cd' P = a±ab±±cc-Fd' G2 is easy to compute because the binomial coefficients in the numerator and in the denominator cancel each other out. All my methods initialize the parameters score(u,v) to G2(u, v), except that any pairing with NULL is initialized to an infinitesimal value.

Background

acl_arc_intent_classification:train:1644

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: For right-branching structures, the leftcorner ancestor is the parent, conditioning on which has been found to be beneficial (Johnson, 1998), as has conditioning on the left-corner child (Roark and Johnson, 1999). These nodes are the left-corner ancestor of top, (which is below top, on the stack), top 's left-corner child (its leftmost child, if any), and top 's most recent child (which was top,_1, if any). For this reason, D(top) includes nodes which are structurally local to top,. Citation Sentence: For right-branching structures , the leftcorner ancestor is the parent , conditioning on which has been found to be beneficial ( Johnson , 1998 ) , as has conditioning on the left-corner child ( Roark and Johnson , 1999 ) . Context after the citation: Because these inputs include the history features of both the leftcorner ancestor and the most recent child, a derivation step i always has access to the history features from the previous derivation step i — 1, and thus (by induction) any information from the entire previous derivation history could in principle be stored in the history features. Thus this model is making no a priori hard independence assumptions, just a priori soft biases. As mentioned above, D(top) also includes top, itself, which means that the inputs to g always include the history features for the most recent derivation step assigned to top,. This input imposes an appropriate bias because the induced history features which are relevant to previous derivation decisions involving top, are likely to be relevant to the decision at step i as well.

Background

acl_arc_intent_classification:train:1645

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: This strategy is certainly the right one to start out with, since anaphora is always the more typical direction of reference in English prose (Halliday and Hasan 1976, p. 329). This means that the "it" that brought the disease in P1 will not be considered to refer to the infection "i" or the death "d" in P3. Note: In our translation from English to logic we are assuming that "it" is anaphoric (with the pronoun following the element that it refers to), not cataphoric (the other way around). Citation Sentence: This strategy is certainly the right one to start out with , since anaphora is always the more typical direction of reference in English prose ( Halliday and Hasan 1976 , p. 329 ) . Context after the citation: Since techniques developed elsewhere may prove useful, at least for comparison, it is worth mentioning at this point that the proposed metarules are distant cousins of "unique-name assumption" (Genesereth and Nilsson 1987), "domain closure assumption" (ibid.) , "domain circumscription" (cfXXX Etherington and Mercer 1987), and their kin. Similarly, the notion of R + M-abduction is spiritually related to the "abductive inference" of Reggia (1985), the "diagnosis from first principles" of Reiter (1987), "explainability" of Poole (1988), and the subset principle of Berwick (1986). But, obviously, trying to establish precise connections for the metarules or the provability and the R + M-abduction would go much beyond the scope of an argument for the correspondence of paragraphs and models.

Motivation

acl_arc_intent_classification:train:1646

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: This indicates that parse trees are usually not the optimal choice for training tree-based translation models (Wang et al., 2010). 2) Parse trees are actually only used to model and explain the monolingual structure, rather than the bilingual mapping between language pairs. However, for many language pairs, it is difficult to acquire such corresponding linguistic parsers due to the lack of Tree-bank resources for training. Citation Sentence: This indicates that parse trees are usually not the optimal choice for training tree-based translation models ( Wang et al. , 2010 ) . Context after the citation: Based on the above analysis, we can conclude that the tree structure that is independent from Tree-bank resources and simultaneously considers the bilingual mapping inside the bilingual sentence pairs would be a good choice for building treebased translation models. Therefore, complying with the above conditions, we propose an unsupervised tree structure for treebased translation models in this study. In the structures, tree nodes are labeled by combining the word classes of their boundary words rather than by syntactic labels, such as NP, VP. Furthermore, using these node labels, we design a generative Bayesian model to infer the final tree structure based on synchronous tree substitution grammars (STSG) 2 .

Background

acl_arc_intent_classification:train:1647

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: In Charniak (1996) and Krotov et al. (1998), it was observed that treebank grammars (CFGs extracted from treebanks) are very large and grow with the size of the treebank. The rate of accession may also be represented graphically. Table 27 shows that the most common case is that of known verbs occurring with a different, although known, subcategorization frame (7.85%). Citation Sentence: In Charniak ( 1996 ) and Krotov et al. ( 1998 ) , it was observed that treebank grammars ( CFGs extracted from treebanks ) are very large and grow with the size of the treebank . Context after the citation: We were interested in discovering whether the acquisition of lexical material from the same data displayed a similar propensity. Figure 8 graphs the rate of induction of semantic form and CFG rule types from Penn-III (the WSJ and parse-annotated Brown corpus combined). Because of the variation in the size of sections between the Brown and the WSJ, we plotted accession against word count. The first part of the graph (up to 1,004,414 words)

Background

acl_arc_intent_classification:train:1648

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: For example, the forward-backward algorithm (Baum, 1972) trains only Hidden Markov Models, while (Ristad and Yianilos, 1996) trains only stochastic edit distance. Not only do these methods require additional programming outside the toolkit, but they are limited to particular kinds of models and training regimens. Currently, finite-state practitioners derive weights using exogenous training methods, then patch them onto transducer arcs. Citation Sentence: For example , the forward-backward algorithm ( Baum , 1972 ) trains only Hidden Markov Models , while ( Ristad and Yianilos , 1996 ) trains only stochastic edit distance . Context after the citation: In short, current finite-state toolkits include no training algorithms, because none exist for the large space of statistical models that the toolkits can in principle describe and run. 'Given output, find input to maximize P(input, output). This paper aims to provide a remedy through a new paradigm, which we call parameterized finitestate machines. It lays out a fully general approach for training the weights of weighted rational relations.

Background

acl_arc_intent_classification:train:1649

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: In fact, most of the features3 implemented in existing coreference resolution systems rely solely on mention heads (Bengtson and Roth, 2008). In both cases, the mention heads are sufficient to support the decisions: ”they” refers to ”companies”, and ”They” refers to ”manufacturers”. In the example above, the first “they” refers to “Multinational companies investing in China” and the second “They” refers to “Domestic manufacturers, who are also suffering”. Citation Sentence: In fact , most of the features3 implemented in existing coreference resolution systems rely solely on mention heads ( Bengtson and Roth , 2008 ) . Context after the citation: Furthermore, consider the possible mention candidate “league” (italic in the text). It is not chosen as a mention because the surrounding context is not focused on “anti-piracy league”. So, mention the CoNLL-2012 dataset is built from OntoNotes-5.0 corpus.

Background

acl_arc_intent_classification:train:165

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: ment (Sarkar and Wintner, 1999; Doran et al., 2000; Makino et al., 1998). 1In this paper, we use the term LTAG to refer to FBLTAG, if not confusing. There have been many studies on parsing techniques (Poller and Becker, 1998; Flickinger et al., 2000), ones on disambiguation models (Chiang, 2000; Kanayama et al., 2000), and ones on programming/grammar-development environ- Citation Sentence: ment ( Sarkar and Wintner , 1999 ; Doran et al. , 2000 ; Makino et al. , 1998 ) . Context after the citation: These works are restricted to each closed community, and the relation between them is not well discussed. Investigating the relation will be apparently valuable for both communities. In this paper, we show that the strongly equivalent grammars enable the sharing of “parsing techniques”, which are dependent on each computational framework and have never been shared among HPSG and LTAG communities. We apply our system to the latest version of the XTAG English grammar (The XTAG Research Group, 2001), which is a large-scale FB-LTAG grammar.

Background

acl_arc_intent_classification:train:1650

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Manning (1993) argues that, aside from missing domain-specific complementation trends, dictionaries produced by hand will tend to lag behind real language use because of their static nature. In addition, subcategorization requirements may vary across linguistic domain or genre (Carroll and Rooth 1998). However, as the manual construction of lexical resources is time consuming, error prone, expensive, and rarely ever complete, it is often the case that the limitations of NLP systems based on lexicalized approaches are due to bottlenecks in the lexicon component. Citation Sentence: Manning ( 1993 ) argues that , aside from missing domain-specific complementation trends , dictionaries produced by hand will tend to lag behind real language use because of their static nature . Context after the citation: Given these facts, research on automating acquisition of dictionaries for lexically based NLP systems is a particularly important issue. Aside from the extraction of theory-neutral subcategorization lexicons, there has also been work in the automatic construction of lexical resources which comply with the principles of particular linguistic theories such as LTAG, CCG, and HPSG (Chen and Vijay-Shanker 2000; Xia 1999; Hockenmaier, Bierner, and Baldridge 2004; Nakanishi, Miyao, and Tsujii 2004). In this article we present an approach to automating the process of lexical acquisition for LFG (i.e., grammatical-function-based systems). However, our approach also generalizes to CFG category-based approaches.

Motivation

acl_arc_intent_classification:train:1651

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: Finally, the Natural Language Toolkit (NLTK) is a package of NLP components implemented in Python (Loper and Bird, 2002). This library has been very popular because it allows researchers to experiment with different methods without having to modify code or reformat data. The Weka package (Witten and Frank, 1999) provides a common framework for several existing machine learning methods including decision trees and support vector machines. Citation Sentence: Finally , the Natural Language Toolkit ( NLTK ) is a package of NLP components implemented in Python ( Loper and Bird , 2002 ) . Context after the citation: Python scripting is extremely simple to learn, read and write, and so using the existing components and designing new components is simple.

Background

acl_arc_intent_classification:train:1652

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: Politically-oriented text Sentiment analysis has specifically been proposed as a key enabling technology in eRulemaking, allowing the automatic analysis of the opinions that people submit (Shulman et al., 2005; Cardie et al., 2006; Kwon et al., 2006). Citation Sentence: Politically-oriented text Sentiment analysis has specifically been proposed as a key enabling technology in eRulemaking , allowing the automatic analysis of the opinions that people submit ( Shulman et al. , 2005 ; Cardie et al. , 2006 ; Kwon et al. , 2006 ) . Context after the citation: There has also been work focused upon determining the political leaning (e.g., “liberal” vs. “conservative”) of a document or author, where most previously-proposed methods make no direct use of relationships between the documents to be classified (the “unlabeled” texts) (Laver et al., 2003; Efron, 2004; Mullen and Malouf, 2006). An exception is Grefenstette et al. (2004), who experimented with determining the political orientation of websites essentially by classifying the concatenation of all the documents found on that site. Others have applied the NLP technologies of near-duplicate detection and topic-based text categorization to politically oriented text (Yang and Callan, 2005; Purpura and Hillard, 2006). Detecting agreement We used a simple method to learn to identify cross-speaker references indicating agreement.

Background

acl_arc_intent_classification:train:1653

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: Burkett and Klein (2008) and Burkett et al. (2010) focused on joint parsing and alignment. Our U-trees are learned based on STSG, which is more appropriate for tree-based translation models than SCFG. This study differs from their work because we concentrate on constructing tree structures for tree-based translation models. Citation Sentence: Burkett and Klein ( 2008 ) and Burkett et al. ( 2010 ) focused on joint parsing and alignment . Context after the citation: They utilized the bilingual Tree-bank to train a joint model for both parsing and word alignment. Cohn and Blunsom (2009) adopted a Bayesian method to infer an STSG by exploring the space of alignments based on parse trees. Liu et al. (2012) re-trained the linguistic parsers bilingually based on word alignment. Burkett and Klein (2012) utilized a transformation-based method to learn a sequence of monolingual tree transformations for translation.

CompareOrContrast

acl_arc_intent_classification:train:1654

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: But the general outlines are reasonably clear, and we can adapt some of the UDRS (Reyle 1995) work to our own framework. Developing a calculus for reasoning with QLFs is too large a task to be undertaken here. Information extraction systems typically carry out such reasoning in a way that is, in Jerry Hobbs' phrase, unhindered by theory. Citation Sentence: But the general outlines are reasonably clear , and we can adapt some of the UDRS ( Reyle 1995 ) work to our own framework . Context after the citation: Reyle points out that many of the inferences involving underspecified representations that we would like to capture rely on the assumption that whatever context disambiguates the premise also disambiguates the conclusion, even if we do not know what that context or disambiguation is. His example is: If the students get £10 then they buy books. The students get . £10.

Uses

acl_arc_intent_classification:train:1655

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: KUbler, McDonald, and Nivre (2009) describe a “typical” MaltParser model configuration of attributes and features.13 Starting with it, in a series of initial controlled experiments, we settled on using buf[0-1] + stk[0-1] for word-forms, and buf[0-3] + stk[0-2] for POS tags. Hence MaltParser features are defined as POS tag at stk[0], word-form at buf[0], and so on. The most commonly used feature functions are the top of the input buffer (next word to process, denoted buf[0]), or top of the stack (denoted stk[0]); following items on buffer or stack are also accessible (buf[1], buf[2], stk[1], etc.). Citation Sentence: KUbler , McDonald , and Nivre ( 2009 ) describe a `` typical '' MaltParser model configuration of attributes and features .13 Starting with it , in a series of initial controlled experiments , we settled on using buf [ 0-1 ] + stk [ 0-1 ] for word-forms , and buf [ 0-3 ] + stk [ 0-2 ] for POS tags . Context after the citation: For features of new MaltParser-attributes (discussed later), we used buf[0] + stk[0]. We did not change the features for deprel: stk[0], ldep(stk[0]), rdep(stk[0]), ldep(buf[0]), rdep(buf[0]) (where ldep and rdep are the left and right, respectively, dependents of the specified argument). This new MaltParser configuration resulted in gains of 0.3–1.1% in labeled attachment accuracy (depending on the POS tag set) over the default MaltParser configuration. We also experimented with using normalized word-forms (Alif Maqsura conversion to Ya, and Hamza removal from each Alif) as is common in parsing and statistical machine translation literature, but it resulted in a small decrease in performance, so we settled on using non-normalized word-forms.

Uses

acl_arc_intent_classification:train:1656

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: For this evaluation, we randomly selected 50 abstracts with disorders indexed as the main topic from abstracts retrieved using PubMed on the five clinical questions described in Sneiderman et al. (2005). For some abstracts, MeSH headings can be used as ground truth, because one of the human indexers’ tasks in assigning terms is to identify the main topic of the article (sometimes a disorder). Although our problem extractor returns a list of clinical problems, we only evaluate performance on identification of the primary problem. Citation Sentence: For this evaluation , we randomly selected 50 abstracts with disorders indexed as the main topic from abstracts retrieved using PubMed on the five clinical questions described in Sneiderman et al. ( 2005 ) . Context after the citation: We applied our problem extractor on different segments of the abstract: the title only, the title and first two sentences, and the entire abstract. These results are shown in Table 2. Here, a problem was considered correctly identified only if it shared the same concept ID as the ground truth problem (from the MeSH heading). The performance of our best variant (abstract title and first two sentences) approaches the upper bound on MetaMap performance—which is limited by human agreement on the identification of semantic concepts in medical texts, as established in Pratt and Yetisgen-Yildiz (2003).

Uses

acl_arc_intent_classification:train:1657

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: The need for information systems to support physicians at the point of care has been well studied (Covell et al., 1985; Gorman et al., 1994; Ely et al., 2005). For a variety of reasons, medicine is an interesting domain of research. This paper presents experiments with generative content models for analyzing the discourse structure of medical abstracts, which has been confirmed to follow the four-section pattern discussed above (Salanger-Meyer, 1990). Citation Sentence: The need for information systems to support physicians at the point of care has been well studied ( Covell et al. , 1985 ; Gorman et al. , 1994 ; Ely et al. , 2005 ) . Context after the citation: Retrieval techniques can have a large impact on how physicians access and leverage clinical evidence. Information that satisfies physicians’ needs can be found in the MEDLINE database maintained by the U.S. National Library of Medicine (NLM), which also serves as a readily available corpus of abstracts for our experiments. Furthermore, the availability of rich ontological resources, in the form of the Unified Medical Language System (UMLS) (Lindberg et al., 1993), and the availability of software that leverages this knowledge— MetaMap (Aronson, 2001) for concept identification and SemRep (Rindflesch and Fiszman, 2003) for relation extraction—provide a foundation for studying the role of semantics in various tasks.

Background

acl_arc_intent_classification:train:1658

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: This approach is taken in computational syntactic grammars (e.g. Jensen 1986); the number of unlikely parses is severely reduced whenever possible, but no attempt is made to define only the so-called grammatical strings of a language. We can also hope for some fine-tuning of the notion of topic, which would prevent many offensive examples. In our case the concept "black + death," which does not refer to any normal experiences, would be discarded as useless, although the collection of sentences would be recognized as a strange, even if coherent, paragraph. Citation Sentence: This approach is taken in computational syntactic grammars ( e.g. Jensen 1986 ) ; the number of unlikely parses is severely reduced whenever possible , but no attempt is made to define only the so-called grammatical strings of a language . Context after the citation: Finally, as the paragraph is a natural domain in which word senses can be reliably assigned to words or sentences can be syntactically disambiguated, larger chunks of discourse may be needed for precise assignment of topics, which we view as another type of disambiguation. Notice also that for coherence, as defined above, it does not matter whether the topic is defined as a longest, a shortest, or—simply—a sequence of predicates satisfying the conditions (1) and (2); the existence of a sequence is equivalent with the existence of a shortest and a longest sequence. The reason for choosing a longest sequence as the topic is our belief that the topic should rather contain more information about a paragraph than less.

Background

acl_arc_intent_classification:train:1659

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: The problem of handling ill-formed input has been studied by Carbonell and Hayes (1983), Granger (1983), Jensen et al. (1983), Kwasny and Sondheimer (1981), Riesbeck and Schank (1976), Thompson (1980), Weischedel and Black (1980), and Weischedel and Sondheimer (1983). A detailed description of the kinds of expectation mechanisms appearing in these systems appears in Fink (1983). While some of these systems did exhibit expectation capabilities at the sentence level, none acquired dialogues of the kind described here for the sake of dialogue level expectation and error correction. Citation Sentence: The problem of handling ill-formed input has been studied by Carbonell and Hayes ( 1983 ) , Granger ( 1983 ) , Jensen et al. ( 1983 ) , Kwasny and Sondheimer ( 1981 ) , Riesbeck and Schank ( 1976 ) , Thompson ( 1980 ) , Weischedel and Black ( 1980 ) , and Weischedel and Sondheimer ( 1983 ) . Context after the citation: A wide variety of techniques have been developed for addressing problems at the word, phrase, sentence, and in some cases, dialogue level. However, these methodologies have not used historical information at the dialogue level as described here. In most cases, the goal of these systems is to characterize the ill-formed input into classes of errors and to correct on that basis. The work described here makes no attempt to classify the errors, but treats them as random events that occur at any point in a sentence.

CompareOrContrast

acl_arc_intent_classification:train:166

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Previous work on Chinese SRL mainly focused on how to transplant the machine learning methods which has been successful with English, such as Sun and Jurafsky (2004), Xue and Palmer (2005) and Xue (2008). Compared to the research on English, the research on Chinese SRL is still in its infancy stage. With the efforts of many researchers (Carreras and Màrquez 2004, 2005, Moschitti 2004, Pradhan et al 2005, Zhang et al 2007), different machine learning methods and linguistics resources are applied in this task, which has made SRL task progress fast. Citation Sentence: Previous work on Chinese SRL mainly focused on how to transplant the machine learning methods which has been successful with English , such as Sun and Jurafsky ( 2004 ) , Xue and Palmer ( 2005 ) and Xue ( 2008 ) . Context after the citation: Sun and Jurafsky (2004) did the preliminary work on Chinese SRL without any large semantically annotated corpus of Chinese. They just labeled the predicate-argument structures of ten specified verbs to a small collection of Chinese sentences, and used Support Vector Machines to identify and classify the arguments. This paper made the first attempt on Chinese SRL and produced promising results. After the PropBank (Xue and Palmer 2003) was built, Xue and Palmer (2005) and Xue (2008) have produced more complete and systematic research on Chinese SRL.

Background

acl_arc_intent_classification:train:1660

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: For example, the interaction of lexical rules is explored at run-time, even though the possible interaction can be determined at compile-time given the information available in the lexical rules and the base lexical entries.2 Based on the research results reported in Meurers and Minnen (1995, 1996), we propose a new computational treatment of lexical rules that overcomes these shortcomings and results in a more efficient processing of lexical rules as used in HPSG. In addition, computationally treating lexical rules on a par with phrase structure rules fails to take computational advantage of their specific properties. Treatments of lexical rules as unary phrase structure rules also require their fully explicit specification, which entails the last problem mentioned above. Citation Sentence: For example , the interaction of lexical rules is explored at run-time , even though the possible interaction can be determined at compile-time given the information available in the lexical rules and the base lexical entries .2 Based on the research results reported in Meurers and Minnen ( 1995 , 1996 ) , we propose a new computational treatment of lexical rules that overcomes these shortcomings and results in a more efficient processing of lexical rules as used in HPSG . Context after the citation: We developed a compiler that takes as its input a set of lexical rules, deduces the necessary transfer of properties not changed by the individual lexical rules, and encodes the set of lexical rules and their interaction into definite relations constraining lexical entries. Each lexical entry is automatically extended with a definite clause encoding of the lexical rule applications which the entry can undergo. The definite clauses thereby introduce what we refer to as systematic covariation in lexical entries. Definite relations are a convenient way of encoding the interaction of lexical rules, as they readily support various program transformations to improve the encoding: We show that the definite relations produced by the compiler can be refined by program transformation techniques to increase efficiency.

Motivation

acl_arc_intent_classification:train:1661

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: "Coherence," as outlined above, can be understood as a declarative (or static) version of marker passing (Hirst 1987; Charniak 1983), with one difference: the activation spreads to theories that share a predicate, not through the IS-A hierarchy, and is limited to elementary facts about predicates appearing in the text. The idea of using preferences among theories is new, hence it was described in more detail. Moreover, in addition to proposing this structure of R, we have described the two mechanisms for exploiting it, "coherence" and "dominance," which are not variants of the standard first order entailment, but abduction. Citation Sentence: `` Coherence , '' as outlined above , can be understood as a declarative ( or static ) version of marker passing ( Hirst 1987 ; Charniak 1983 ) , with one difference : the activation spreads to theories that share a predicate , not through the IS-A hierarchy , and is limited to elementary facts about predicates appearing in the text . Context after the citation: The metalevel rules we are going to discuss in Section 6, and that deal with the Gricean maxims and the meaning of "but," can be easily expressed in the languages of set theory or higher order logic, but not everything expressible in those languages makes sense in natural language. Hence, putting limitations on the expressive power of the language of the metalevel will remain as one of many open problems. 4. Coherence of Paragraphs

CompareOrContrast

acl_arc_intent_classification:train:1662

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: conclusion Context before the citation: On small data sets all of the Bayesian estimators strongly outperform EM (and, to a lesser extent, VB) with respect to all of our evaluation measures, confirming the results reported in Goldwater and Griffiths (2007). As might be expected, our evaluation measures disagree somewhat, but the following broad tendancies seem clear. Citation Sentence: On small data sets all of the Bayesian estimators strongly outperform EM ( and , to a lesser extent , VB ) with respect to all of our evaluation measures , confirming the results reported in Goldwater and Griffiths ( 2007 ) . Context after the citation: This is perhaps not too surprising, as the Bayesian prior plays a comparatively stronger role with a smaller training corpus (which makes the likelihood term smaller) and the approximation used by Variational Bayes is likely to be less accurate on smaller data sets. But on larger data sets, which Goldwater et al did not study, the results are much less clear, and depend on which evaluation measure is used. Expectation Maximization does surprisingly well on larger data sets and is competitive with the Bayesian estimators at least in terms of cross-validation accuracy, confirming the results reported by Johnson (2007). Variational Bayes converges faster than all of the other estimators we examined here.

CompareOrContrast

acl_arc_intent_classification:train:1663

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: In addition, the advantages of using linguistically annotated data over raw data are well documented (Mair, 2005; Granger and Rayson, 1998). This topic generated intense interest at workshops held at the University of Heidelberg (October 2004), University of Bologna (January 2005), University of Birmingham (July 2005) and now in Trento in April 2006. Increasingly, corpus researchers are tapping the Web to overcome the sparse data problem (Keller et al., 2002). Citation Sentence: In addition , the advantages of using linguistically annotated data over raw data are well documented ( Mair , 2005 ; Granger and Rayson , 1998 ) . Context after the citation: As the size of a corpus increases, a near linear increase in computing power is required to annotate the text. Although processing power is steadily growing, it has already become impractical for a single computer to annotate a mega-corpus. Creating a large-scale annotated corpus from the web requires a way to overcome the limitations on processing power. We propose distributed techniques to alleviate the limitations on the

Background

acl_arc_intent_classification:train:1664

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: For the A* algorithm (Hart et al. 1968) as applied to speech recognition, the actual path score is typically augmented with an estimated score for the unseen portion. 10 Some modification of this scheme is necessary when the input stream is not deterministic. A final alternative is to include a PARTICLE bit among Citation Sentence: For the A * algorithm ( Hart et al. 1968 ) as applied to speech recognition , the actual path score is typically augmented with an estimated score for the unseen portion . Context after the citation: Unless some kind of normalization is done, the short theories have an unfair advantage, simply because fewer probability scores have been multiplied. With a deterministic word sequence it seems reasonable to assume probability 1.0 for what has been found. 11 The auxiliary verb sets the mode of the main verb to be root or past participle as appropriate. the features which, once set, cannot be reset.

Uses

acl_arc_intent_classification:train:1665

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Robust natural language understanding in Atlas-Andes is provided by Rosé's CARMEL system (Rosé 2000); it uses the spelling correction algorithm devised by Elmi and Evens (1998). Figure 4 shows the architecture of Atlas-Andes; any other system built with APE would look similar. The first system we have implemented with APE is a prototype Atlas-Andes system that replaces the hints usually given for an incorrect acceleration vector by a choice of generated subdialogues. Citation Sentence: Robust natural language understanding in Atlas-Andes is provided by Rosé 's CARMEL system ( Rosé 2000 ) ; it uses the spelling correction algorithm devised by Elmi and Evens ( 1998 ) . Context after the citation:

Uses

acl_arc_intent_classification:train:1666

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: The framework represents a generalization of several predecessor NLG systems based on Meaning-Text Theory: FoG (Kittredge and Polguere, 1991), LFS (Iordanskaja et al., 1992), and JOYCE (Rambow and Korelsky, 1992). 8 History of the Framework and Comparison with Other Systems In general, a better integration of linguistically based and statistical methods during all the development phases is greatly needed. Citation Sentence: The framework represents a generalization of several predecessor NLG systems based on Meaning-Text Theory : FoG ( Kittredge and Polguere , 1991 ) , LFS ( Iordanskaja et al. , 1992 ) , and JOYCE ( Rambow and Korelsky , 1992 ) . Context after the citation: The framework was originally developed for the realization of deep-syntactic structures in NLG (Lavoie and Rambow, 1997). It was later extended for generation of deep-syntactic structures from conceptual interlingua (Kittredge and Lavoie, 1998). Finally, it was applied to MT for transfer between deep-syntactic structures of different languages (Palmer et al., 1998). The current framework encompasses the full spectrum of such transformations, i.e. from the processing of conceptual structures to the processing of deep-syntactic structures, either for NLG or MT. Compared to its predecessors (Fog, LFS, JOYCE), our approach has obvious advantages in uniformity, declarativity and portability.

Extends

acl_arc_intent_classification:train:1667

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Nevertheless, Juola (1998, page 23) observes that “a slightly more general mapping, where two adjacent terminal symbols can be merged into a single lexical item (for example, a word and its case-marking), can capture this sort of result quite handily.” However, marker-normal form grammars cannot capture the sorts of regularities demonstrated for languages that do not have a oneto-one mapping between a terminal symbol and a word. Juola’s (1994, 1998) work on grammar optimization and induction shows that context-free grammars can be converted to ”marker-normal form.” Citation Sentence: Nevertheless , Juola ( 1998 , page 23 ) observes that `` a slightly more general mapping , where two adjacent terminal symbols can be merged into a single lexical item ( for example , a word and its case-marking ) , can capture this sort of result quite handily . '' Context after the citation: Work using the marker hypothesis for MT adapts this monolingual mapping for pairs of languages: It is reasonably straightforward to map an English determiner-noun sequence onto a Japanese noun–case marker segment, once one has identified the sets of marker tags in the languages to be translated. Following construction of the marker lexicon, the (source, target) chunks are generalized further using a methodology based on Block (2000) to permit a limited form of insertion in the translation process. As a byproduct of the chosen methodology, we also derive a standard ”word-level” translation lexicon. These various resources render the set of original translation pairs far more useful in deriving translations of previously unseen input.

Background

acl_arc_intent_classification:train:1668

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Other similar approaches include those of Cicekli and G¨uvenir (1996), McTait and Trujillo (1999), Carl (1999), and Brown (2000), inter alia. Watanabe (1993) combines lexical and dependency mappings to form his generalizations. Kaji, Kida, and Morimoto (1992) identify translationally equivalent phrasal segments and replace such equivalents with variables to generate a set of translation patterns. Citation Sentence: Other similar approaches include those of Cicekli and G ¨ uvenir ( 1996 ) , McTait and Trujillo ( 1999 ) , Carl ( 1999 ) , and Brown ( 2000 ) , inter alia . Context after the citation: In our system, in some cases the smallest chunk obtainable via the marker-based segmentation process may be something like (27): (27) <DET> the good man: le bon homme In such cases, if our system were confronted with a good man, it would not be able to translate such a phrase, assuming this to be missing from the marker lexicon. Accordingly, we convert examples such as (27) into their generalized equivalents, as in (28): (28) <DET> good man: bon homme That is, where Block (2000) substitutes variables for various words in his templates, we replace certain lexical items with their marker tag. Given that examples such as ’‘<DET> a : un” are likely to exist in the word-level lexicon, they may be inserted at the point indicated by the marker tag to form the correct translation un bon homme. We thus cluster on marker words to improve the coverage of our system (see Section 5 for results that show exactly how clustering on marker words helps); others (notably Brown [2000, 2003]) use clustering techniques to determine equivalence classes of individual words that can occur in the same context, and in so doing derive translation templates from individual translation examples.

Background

acl_arc_intent_classification:train:1669

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: In most recent research, NEs (person, location and organisations) are extracted from the text and used as a source of evidence to calculate the similarity between documents -see for instance (Blume, 2005; Chen and Martin, 2007; Popescu and Magnini, 2007; Kalashnikov et al., 2007). Ravin (1999) introduced a rule-based approach that tackles both variation and ambiguity analysing the structure of names. The most used feature for the Web People Search task, however, are NEs. Citation Sentence: In most recent research , NEs ( person , location and organisations ) are extracted from the text and used as a source of evidence to calculate the similarity between documents - see for instance ( Blume , 2005 ; Chen and Martin , 2007 ; Popescu and Magnini , 2007 ; Kalashnikov et al. , 2007 ) . Context after the citation: For instance, Blume (2005) uses NEs coocurring with the ambiguous mentions of a name as a key feature for the disambiguation process. Saggion (2008) compared the performace of NEs versus BoW features. In his experiments a only a representation based on Organisation NEs outperformed the word based approach. Furthermore, this result is highly dependent on the choice of metric weighting (NEs achieve high precision at the cost of a low recall and viceversa for BoW).

Background

acl_arc_intent_classification:train:167

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: (Watanabe et al., 2007; Chiang et al., 2008; Hopkins and May, 2011) proposed other optimization objectives by introducing a margin-based and ranking-based indirect loss functions. (Och and Ney, 2002; Blunsom et al., 2008) used maximum likelihood estimation to learn weights for MT. (Och, 2003; Moore and Quirk, 2008; Zhao and Chen, 2009; Galley and Quirk, 2011) employed an evaluation metric as a loss function and directly optimized it. Several works have proposed discriminative techniques to train log-linear model for SMT. Citation Sentence: ( Watanabe et al. , 2007 ; Chiang et al. , 2008 ; Hopkins and May , 2011 ) proposed other optimization objectives by introducing a margin-based and ranking-based indirect loss functions . Context after the citation: All the methods mentioned above train a single weight for the whole development set, whereas our local training method learns a weight for each sentence. Further, our translation framework integrates the training and testing into one unit, instead of treating them separately. One of the advantages is that it can adapt the weights for each of the test sentences. Our method resorts to some translation examples, which is similar as example-based translation or translation memory (Watanabe and Sumita, 2003; He et al., 2010; Ma et al., 2011).

CompareOrContrast

acl_arc_intent_classification:train:1670

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: Burkett and Klein (2012) utilized a transformation-based method to learn a sequence of monolingual tree transformations for translation. Liu et al. (2012) re-trained the linguistic parsers bilingually based on word alignment. Cohn and Blunsom (2009) adopted a Bayesian method to infer an STSG by exploring the space of alignments based on parse trees. Citation Sentence: Burkett and Klein ( 2012 ) utilized a transformation-based method to learn a sequence of monolingual tree transformations for translation . Context after the citation: Compared to their work, we do not rely on any Tree-bank resources and focus on generating effective unsupervised tree structures for tree-based translation models. Zollmann and Venugopal (2006) substituted the non-terminal X in hierarchical phrase-based model by extended syntactic categories. Zollmann and Vogel (2011) further labeled the SCFG rules with POS tags and unsupervised word classes. Our work differs from theirs in that we present a Bayesian model to learn effective STSG translation rules and U-tree structures for tree-based translation models, rather than designing a labeling strategy for translation rules.

CompareOrContrast

acl_arc_intent_classification:train:1671

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: Baseline language model: For P0 we used a trigram with modified kneser-ney smoothing [Chen and Goodman, 1998], which is still considered one of the best smoothing methods for n-gram language models. Our learning framework leaves open a number of design choices: 1. The resulting lexicon contained 603 word types. Citation Sentence: Baseline language model : For P0 we used a trigram with modified kneser-ney smoothing [ Chen and Goodman , 1998 ] , which is still considered one of the best smoothing methods for n-gram language models . Context after the citation: 2. Sentence representation: Each sentence was represented as the collection of unigrams, bigrams and trigrams it contained. A coordinate was reserved for each such n-gram which appeared in the data, whether real or sampled. The value of the n'th coordinate in the vector representation of 4 Interestingly, in practice both methods result in near identical rejection probabilities, within a precision of 0.0001.

Uses

acl_arc_intent_classification:train:1672

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: Therefore, we preprocess Ontonote-5.0 to derive mention heads using Collins head rules (Collins, 1999) with gold constituency parsing information and gold named entity information. The ACE-2004 dataset is annotated with both mention and mention heads, while the OntoNotes5.0 dataset only has mention annotations. We report results on the test documents for both datasets. Citation Sentence: Therefore , we preprocess Ontonote-5 .0 to derive mention heads using Collins head rules ( Collins , 1999 ) with gold constituency parsing information and gold named entity information . Context after the citation: The parsing information9 is only needed to generate training data for the mention head candidate generator and named entities are directly set as heads. We set these extracted heads as gold, which enables us to train the two layer BILOU-classifier described in Sec. 3.1.1. The nonoverlapping mention head assumption in Sec.

Uses

acl_arc_intent_classification:train:1673

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: conclusion Context before the citation: Our most accurate single grammar achieves an F score of 91.6 on the WSJ test set, rivaling discriminative reranking approaches (Charniak and Johnson, 2005) and products of latent variable grammars (Petrov, 2010), despite being a single generative PCFG. Second, the diversity of the individual grammars controls the gains that can be obtained by combining multiple grammars into a product model. First, the accuracy of the model used for parsing the unlabeled data is important for the accuracy of the resulting single self-trained grammars. Citation Sentence: Our most accurate single grammar achieves an F score of 91.6 on the WSJ test set , rivaling discriminative reranking approaches ( Charniak and Johnson , 2005 ) and products of latent variable grammars ( Petrov , 2010 ) , despite being a single generative PCFG . Context after the citation: Our most accurate product model achieves an F score of 92.5 without the use of discriminative reranking and comes close to the best known numbers on this test set (Zhang et al., 2009). In future work, we plan to investigate additional methods for increasing the diversity of our selftrained models. One possibility would be to utilize more unlabeled data or to identify additional ways to bias the models. It would also be interesting to determine whether further increasing the accuracy of the model used for automatically labeling the unlabeled data can enhance performance even more.

CompareOrContrast

acl_arc_intent_classification:train:1674

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: conclusion Context before the citation: We have presented an ensemble approach to word sense disambiguation (Pedersen, 2000) where multiple Naive Bayesian classifiers, each based on co— occurrence features from varying sized windows of context, is shown to perform well on the widely studied nouns interest and line. One of our long-term objectives is to identify a core set of features that will be useful for disambiguating a wide class of words using both supervised and unsupervised methodologies. Citation Sentence: We have presented an ensemble approach to word sense disambiguation ( Pedersen , 2000 ) where multiple Naive Bayesian classifiers , each based on co -- occurrence features from varying sized windows of context , is shown to perform well on the widely studied nouns interest and line . Context after the citation: While the accuracy of this approach was as good as any previously published results, the learned models were complex and difficult to interpret, in effect acting as very accurate black boxes. Our experience has been that variations in learning algorithms are far less significant contributors to disambiguation accuracy than are variations in the feature set. In other words, an informative feature set will result in accurate disambiguation when used with a wide range of learning algorithms, but there is no learning algorithm that can perform well given an uninformative or misleading set of features. Therefore, our focus is on developing and discovering feature sets that make distinctions among word senses.

Background

acl_arc_intent_classification:train:1675

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Over the last decade there has been a lot of interest in developing tutorial dialogue systems that understand student explanations (Jordan et al., 2006; Graesser et al., 1999; Aleven et al., 2001; Buckley and Wolska, 2007; Nielsen et al., 2008; VanLehn et al., 2007), because high percentages of selfexplanation and student contentful talk are known to be correlated with better learning in humanhuman tutoring (Chi et al., 1994; Litman et al., 2009; Purandare and Litman, 2008; Steinhauser et al., 2007). Citation Sentence: Over the last decade there has been a lot of interest in developing tutorial dialogue systems that understand student explanations ( Jordan et al. , 2006 ; Graesser et al. , 1999 ; Aleven et al. , 2001 ; Buckley and Wolska , 2007 ; Nielsen et al. , 2008 ; VanLehn et al. , 2007 ) , because high percentages of selfexplanation and student contentful talk are known to be correlated with better learning in humanhuman tutoring ( Chi et al. , 1994 ; Litman et al. , 2009 ; Purandare and Litman , 2008 ; Steinhauser et al. , 2007 ) . Context after the citation: However, most existing systems use pre-authored tutor responses for addressing student errors. The advantage of this approach is that tutors can devise remediation dialogues that are highly tailored to specific misconceptions many students share, providing step-by-step scaffolding and potentially suggesting additional problems. The disadvantage is a lack of adaptivity and generality: students often get the same remediation for the same error regardless of their past performance or dialogue context, as it is infeasible to author a different remediation dialogue for every possible dialogue state.

Background

acl_arc_intent_classification:train:1676

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: We shall see this in the next example: two sentences, regarded as a fragment of paragraph, are a variation on a theme by Hobbs (1979). Rather, we stress the possibility that one can axiomatize and productively use such a rule. We do not claim that Gla is the best or unique way of expressing the rule &quot;assume that the writer did not say too much.&quot; Citation Sentence: We shall see this in the next example : two sentences , regarded as a fragment of paragraph , are a variation on a theme by Hobbs ( 1979 ) . Context after the citation:

Extends

acl_arc_intent_classification:train:1677

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: Nevertheless, the full document text is present in most systems, sometimes as the only feature (Sugiyama and Okumura, 2007) and sometimes in combination with others see for instance (Chen and Martin, 2007; Popescu and Magnini, 2007)-. Withindocument coreference resolution has been applied to produce summaries of text surrounding occurrences of the name (Bagga and Baldwin, 1998; Gooi and Allan, 2004). The most basic is a Bag of Words (BoW) representation of the document text. Citation Sentence: Nevertheless , the full document text is present in most systems , sometimes as the only feature ( Sugiyama and Okumura , 2007 ) and sometimes in combination with others see for instance ( Chen and Martin , 2007 ; Popescu and Magnini , 2007 ) - . Context after the citation: Other representations use the link structure (Malin, 2005) or generate graph representations of the extracted features (Kalashnikov et al., 2007). Some researchers (Cucerzan, 2007; Nguyen and Cao, 2008) have explored the use of Wikipedia information to improve the disambiguation process. Wikipedia provides candidate entities that are linked to specific mentions in a text. The obvious limitation of this approach is that only celebrities and historical figures can be identified in this way.

Background

acl_arc_intent_classification:train:1678

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: The recognizer for these systems is the SUMMIT system (Zue et al. 1989), which uses a segmental-based framework and includes an auditory model in the front-end processing. This aspect of the system is beyond the scope of this paper, and therefore it will not be covered in detail. In addition, I will describe briefly how we currently translate the parse tree into a semantic frame that serves as the input to database access and text response generation. Citation Sentence: The recognizer for these systems is the SUMMIT system ( Zue et al. 1989 ) , which uses a segmental-based framework and includes an auditory model in the front-end processing . Context after the citation: The lexicon is entered as phonetic pronunciations that are then augmented to account for a number of phonological rules. The search algorithm is the standard Viterbi search (Viterbi 1967), except that the match involves a network-to-network alignment problem rather than sequence-to-sequence. When we first integrated this recognizer with TINA, we used a &quot;wire&quot; connection, in that the recognizer produced a single best output, which was then passed to TINA for parsing. A simple word-pair grammar constrained the search space.

Uses

acl_arc_intent_classification:train:1679

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: This conception of lexical rules thus can be understood as underlying the computational approach that treats lexical rules as unary phrase structure rules as, for example, adopted in the LKB system (Copestake 1992). Since lexical rules are expressed in the theory just like any other part of the theory, they are represented in the same way, as unary immediate dominance schemata.' Contrary to the MLR setup, the DLR formalization therefore requires all words feeding lexical rules to be grammatical with respect to the theory. Citation Sentence: This conception of lexical rules thus can be understood as underlying the computational approach that treats lexical rules as unary phrase structure rules as , for example , adopted in the LKB system ( Copestake 1992 ) . Context after the citation: Both the input and output of a lexical rule, i.e., the mother and the daughter of a phrase structure rule, are available during a generation or parsing process. As a result, in addition to the information present in the lexical entry, syntactic information can be accessed to execute the constraints on the input of a lexical rule. The computational treatment of lexical rules that we propose in this paper is essentially a domain-specific refinement of such an approach to lexical rules.' 2.2.3 Lexical Rule Specification and Framing.

Background

acl_arc_intent_classification:train:168

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: Some recent GRE algorithms have done away with the separation between content determination and linguistic realization, interleaving the two processes instead (Stone and Webber 1998; Krahmer and Theune 2002). Citation Sentence: Some recent GRE algorithms have done away with the separation between content determination and linguistic realization , interleaving the two processes instead ( Stone and Webber 1998 ; Krahmer and Theune 2002 ) . Context after the citation: We have separated the two phases because, in the case of vague descriptions, interleaving would tend to be difficult. Consider, for instance, the list of properties L = (size > 3 cm, size < 9 cm). If interleaving forced us to realize the two properties in L one by one, then it would no longer be possible to combine them into, for example, the largest mouse but one (if the facts in the KB support it), or even into the mice between 3 and 9 cm (since size > 3 cm is realized before size < 9 cm). Clearly, sophisticated use of gradable adjectives requires a separation between CD and linguistic realization, unless one is willing to complicate linguistic realization considerably.

CompareOrContrast

acl_arc_intent_classification:train:1680

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: To sample from our proposal distribution, we use a blocked Gibbs sampler based on the one proposed by Goodman (1998) and used by Johnson et al. (2007) that samples entire parse trees. Our acceptance step is therefore based on the remaining parameters: the context (θLCTX, θRCTX). The basic idea is that we sample trees according to a simpler proposal distribution Q that approximates the full distribution and for which direct sampling is tractable, and then choose to accept or reject those trees based on the true distribution P. For our model, there is a straightforward and intuitive choice for the proposal distribution: the PCFG model without our context parameters: (θROOT, θBIN, θUN, θTERM, λ), which is known to have an efficient sampling method. Citation Sentence: To sample from our proposal distribution , we use a blocked Gibbs sampler based on the one proposed by Goodman ( 1998 ) and used by Johnson et al. ( 2007 ) that samples entire parse trees . Context after the citation: For a sentence w, the strategy is to use the Inside algorithm (Lari and Young, 1990) to inductively compute, for each potential non-terminal position spanning words wi through wj−1 and category t, going “up” the tree, the probability of generating wi, ... , wj−1 via any arrangement of productions that is rooted by yij = t. · p(wi:j−1 |yij = u) + Et→u v Ei<k<j λt (B) · θtIN(hu, vi) · p(wi:k−1 |yik = u) · p(wk:j−1 |ykj = v) We then pass “downward” through the chart, sampling productions until we reach a terminal word on all branches. where x is either a split point k and pair of categories yik, ykj resulting from a binary rewrite rule, a single category y0ij resulting from a unary rule, or a word w resulting from a terminal rule. The MH procedure requires an acceptance distribution A that is used to accept or reject a tree sampled from the proposal Q.

Uses

acl_arc_intent_classification:train:1681

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: Semantic construction proceeds from the derived tree (Gardent and Kallmeyer, 2003) rather than – as is more common in TAG – from the derivation tree. Thus the semantic representations we assume are simply set of literals of the form Pn(x1, ... , xn) where Pn is a predicate of arity n and xi is either a constant or a unification variable whose value will be instantiated during processing. However because we are here focusing on paraphrases rather than fine grained semantic distinctions, the underspecification and the description of the scope relations permitted by these semantics will here be largely ignored and flat semantics will be principally used as a convenient way of describing predicate/arguments and modifiers/modified relationships. Citation Sentence: Semantic construction proceeds from the derived tree ( Gardent and Kallmeyer , 2003 ) rather than -- as is more common in TAG -- from the derivation tree . Context after the citation: This is done by associating each elementary tree with a semantic representation and by decorating relevant tree nodes with unification variables and constants occuring in associated semantic representation. The association between tree nodes and unification variables encodes the syntax/semantics interface – it specifies which node in the tree provides the value for which variable in the final semantic representation. As trees combine during derivation, (i) variables are unified – both in the tree and in the associated semantic representation – and (ii) the semantics of the derived tree is constructed from the conjunction of the semantics of the combined trees. A simple example will illustrate this.

Background

acl_arc_intent_classification:train:1682

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: It allows the construction of a non-TAL (Shieber, 1994), (Harbusch & Poller, 2000). Considering the original definition of S-TAGs, one can see that it does not restrict the structures that can be produced in the source and target languages. S-TAG is a variant of Tree Adjoining Grammar (TAG) introduced by (Shieber & Schabes,1990) to characterize correspondences between tree adjoining languages. Citation Sentence: It allows the construction of a non-TAL ( Shieber , 1994 ) , ( Harbusch & Poller , 2000 ) . Context after the citation: As a result, Shieber (1994) propose a restricted definition for S-TAG, namely, the IS-TAG for isomorphic S-TAG. In this case only TAL can be formed in each component. This isomorphism requirement is formally attractive, but for practical applications somewhat too strict. Also contrastive well-known translation phenomena exist in different languages, which cannot be expressed by IS-TAG, Figure 3 illustrates some examples (Shieber, 1994).

Background

acl_arc_intent_classification:train:1683

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: Following previous work (e.g., Soon et al. (2001) and Ponzetto and Strube (2006)), we generate training instances as follows: a positive instance is created for each anaphoric NP, NPj, and its closest antecedent, NPi; and a negative instance is created for NPj paired with each of the intervening NPs, NPi+1, NPi+2, ..., NPj_1. Our baseline coreference system uses the C4.5 decision tree learner (Quinlan, 1993) to acquire a classifier on the training texts for determining whether two NPs are coreferent. Citation Sentence: Following previous work ( e.g. , Soon et al. ( 2001 ) and Ponzetto and Strube ( 2006 ) ) , we generate training instances as follows : a positive instance is created for each anaphoric NP , NPj , and its closest antecedent , NPi ; and a negative instance is created for NPj paired with each of the intervening NPs , NPi +1 , NPi +2 , ... , NPj_1 . Context after the citation: Each instance is represented by 33 lexical, grammatical, semantic, and positional features that have been employed by highwe can see, the baseline achieves an F-measure of performing resolvers such as Ng and Cardie (2002) 57.0 and a resolution accuracy of 48.4. and Yang et al. (2003), as described below. To get a better sense of how strong our baseline Lexical features.

Uses

acl_arc_intent_classification:train:1684

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: TF-IDF (term frequency-inverse document frequency) is one of the widely used feature selection techniques in information retrieval (Yates and Neto, 1999). In this paper, we use TF-IDF (a kind of augmented DF) as a feature selection criterion, in order to ensure results are comparable with those in (Yahyaoui, 2001). On the other hand, (Rogati and Yang, 2002) reports the X2 to produce best performance. Citation Sentence: TF-IDF ( term frequency-inverse document frequency ) is one of the widely used feature selection techniques in information retrieval ( Yates and Neto , 1999 ) . Context after the citation: Specifically, it is used as a metric for measuring the importance of a word in a document within a collection, so as to improve the recall and the precision of the retrieved documents. While the TF measurement concerns the importance of a term in a given document, IDF seeks to measure the relative importance of a term in a collection of documents. The importance of each term is assumed to be inversely proportional to the number of documents that contain that term. TF is given by TFD,t, and it denotes frequency of term t in document D. IDF is given by IDFt = log(N/dft), where N is the number of documents in the collection, and dft is the number of documents containing the term t. (Salton and Yang, 1973) proposed the combination of TF and IDF as weighting schemes, and it has been shown that their product gave better performance.

Background

acl_arc_intent_classification:train:1685

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: The gap mechanism resembles the Hold register idea of ATNs (Woods 1970) and the treatment of bounded domination metavariables in lexical functional grammars (LFGs) (Bresnan 1982, p. 235 ff.) (Chomsky 1977). Stephanie Seneff TINA: A Natural Language System for Spoken Language Applications subject-tagging for verbs, and long distance movement (often referred to as gaps, or the trace, as in &quot;(which article), do you think I should read (t1)?&quot;) Citation Sentence: The gap mechanism resembles the Hold register idea of ATNs ( Woods 1970 ) and the treatment of bounded domination metavariables in lexical functional grammars ( LFGs ) ( Bresnan 1982 , p. 235 ff . ) Context after the citation: , but it is different from these in that the process of filling the Hold register equivalent involves two steps separately initiated by two independent nodes. Our approach to the design of a constraint mechanism is to establish a framework general enough to handle syntactic, semantic, and, ultimately, phonological constraints using identical functional procedures applied at the node level. The intent was to design a grammar for which the rules would be kept completely free of any constraints. To achieve this goal, we decided to break the constraint equations usually associated with rules down into their component parts, and then to attach constraints to nodes (i.e., categories) as equations in a single variable.

CompareOrContrast

acl_arc_intent_classification:train:1686

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: method Context before the citation: To solve these scaling issues, we implement Online Variational Bayesian Inference (Hoffman et al., 2010; Hoffman et al., 2012) for our models. Prior work using mLDA has used Gibbs Sampling to approximate the posterior, but we found this method did not scale with larger values of K, especially when applied to the relatively large deWaC corpus. Analytical inference of the posterior distribution of mLDA is intractable, and must be approximated. Citation Sentence: To solve these scaling issues , we implement Online Variational Bayesian Inference ( Hoffman et al. , 2010 ; Hoffman et al. , 2012 ) for our models . Context after the citation: In Variational Bayesian Inference (VBI), one approximates the true posterior using simpler distributions with free variables. The free variables are then optimized in an EM-like algorithm to minimize difference between the true and approximate posteriors. Online VBI differs from normal VBI by using randomly sampled minibatches in each EM step rather than the entire data set. Online VBI easily scales and quickly converges in all of our experiments.

Uses

acl_arc_intent_classification:train:1687

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Typical letter-to-sound rule sets are those described by Ainsworth (1973), McIlroy (1973), Elovitz et al. (1976), Hurmicutt (1976), and Divay and Vitale (1997). Conventionally, these rules are specified by an expert linguist, conversant with the sound and spelling systems of the language of concern. They also constitute a formal model of universal computation (Post 1943). Citation Sentence: Typical letter-to-sound rule sets are those described by Ainsworth ( 1973 ) , McIlroy ( 1973 ) , Elovitz et al. ( 1976 ) , Hurmicutt ( 1976 ) , and Divay and Vitale ( 1997 ) . Context after the citation: Because of the complexities of English spelling-to-sound correspondence detailed in the previous section, more than one rule generally applies at each stage of transcription. The potential conflicts that arise are resolved by maintaining the rules in a set of sublists, grouped by (initial) letter and with each sublist ordered by specificity. Typically, the most specific rule is at the top and most general at the bottom. In the Elovitz et al. rules, for instance, transcription is a one-pass, left-to-right process.

Background

acl_arc_intent_classification:train:169

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: This recognizer incrementally outputs word hypotheses as soon as they are found in the best-scored path in the forward search (Hirasawa et al., 1998) using the ISTAR (Incremental Structure Transmitter And Receiver) protocol, which conveys word graph information as well as word hypotheses. Acoustic models for HTK is trained with the continuous speech database of the Acoustical Society of Japan (Kobayashi et al., 1992). As the recognition engine, either VoiceRex, developed by NTT (Noda et al., 1998), or HTK from Entropic Research can be used. Citation Sentence: This recognizer incrementally outputs word hypotheses as soon as they are found in the best-scored path in the forward search ( Hirasawa et al. , 1998 ) using the ISTAR ( Incremental Structure Transmitter And Receiver ) protocol , which conveys word graph information as well as word hypotheses . Context after the citation: This incremental output allows the language understanding module to process recognition results before the speech interval ends, and thus real-time responses are possible. This module continuously runs and outputs recognition results when it detects a speech interval. This enables the language generation module to react immediately to user interruptions while the system is speaking. The language model for speech recognition is a network (regular) grammar, and it allows each speech interval to be an arbitrary number of phrases.

Background

acl_arc_intent_classification:train:17

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: method Context before the citation: Venables and Ripley (1994) describe an efficient algorithm (of linear complexity in the number of training sentences) for computing the LDA transform matrix, which entails computing the withinand between-covariance matrices of the classes, and using Singular Value Decomposition (SVD) to compute the eigenvectors of the new space. In an attempt to further boost performance, we employed Linear Discriminant Analysis (LDA) to find a linear projection of the four-dimensional vectors that maximizes the separation of the Gaussians (corresponding to the HMM states). For testing, we performed a Viterbi (maximum likelihood) estimation of the label of each test sentence/vector (also using the HTK toolkit). Citation Sentence: Venables and Ripley ( 1994 ) describe an efficient algorithm ( of linear complexity in the number of training sentences ) for computing the LDA transform matrix , which entails computing the withinand between-covariance matrices of the classes , and using Singular Value Decomposition ( SVD ) to compute the eigenvectors of the new space . Context after the citation: Each sentence/vector is then multiplied by this matrix, and new HMM models are re-computed from the projected data. An important aspect of our work is modeling content structure using generative techniques. To assess the impact of taking discourse transitions into account, we compare our fully trained model to one that does not take advantage of the Markov assumption—i.e., it assumes that the labels are independently and identically distributed. To facilitate comparison with previous work, we also experimented with binary classifiers specifically tuned to each section.

Uses

acl_arc_intent_classification:train:170

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: conclusion Context before the citation: For better comparison with work of others, we adopt the suggestion made by Green and Manning (2010) to evaluate the parsing quality on sentences up to 70 tokens long. Citation Sentence: For better comparison with work of others , we adopt the suggestion made by Green and Manning ( 2010 ) to evaluate the parsing quality on sentences up to 70 tokens long . Context after the citation: We report these filtered results in Table 14. Filtered results are consistently higher (as expected). Results are about 0.9% absolute higher on the development set, and about 0.6% higher on the test set. The contribution of the RAT feature across sets is negligible (or small and unstable), resulting in less than 0.1% absolute loss on the dev set, but about 0.15% gain on the test set.

Uses

acl_arc_intent_classification:train:171

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: Some examples include text categorization (Lewis and Catlett 1994), base noun phrase chunking (Ngai and Yarowsky 2000), part-of-speech tagging (Engelson Dagan 1996), spelling confusion set disambiguation (Banko and Brill 2001), and word sense disambiguation (Fujii et al. 1998). applications. In addition to PP-attachment, as discussed in this article, sample selection has been successfully applied to other classification Citation Sentence: Some examples include text categorization ( Lewis and Catlett 1994 ) , base noun phrase chunking ( Ngai and Yarowsky 2000 ) , part-of-speech tagging ( Engelson Dagan 1996 ) , spelling confusion set disambiguation ( Banko and Brill 2001 ) , and word sense disambiguation ( Fujii et al. 1998 ) . Context after the citation: More challenging are learning problems whose objective is not classification, but generation of complex structures. One example in this direction is applying sample selection to semantic parsing (Thompson, Califf, and Mooney 1999), in which sentences are paired with their semantic representation using a deterministic shift-reduce parser. A recent effort that focuses on statistical syntactic parsing is the work by Tang, Lou, and Roukos (2002). Their results suggest that the number of training examples can be further reduced by using a hybrid evaluation function that combines a hypothesisperformance-based metric such as tree entropy (“word entropy” in their terminology) with a problem-space-based metric such as sentence clusters.

Background

acl_arc_intent_classification:train:172

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: method Context before the citation: Differently, Cohn and Blunsom (2009) designed a sampler to infer an STSG by fixing the tree structure and exploring the space of alignment. In addition, it should be noted that the word alignment is fixed8, and we only explore the entire space of tree structures in our sampler. For example, the bold italic nodes with shadows in Figure 2 are frontier nodes. Citation Sentence: Differently , Cohn and Blunsom ( 2009 ) designed a sampler to infer an STSG by fixing the tree structure and exploring the space of alignment . Context after the citation: We believe that it is possible to investigate the space of both tree structure and alignment simultaneously. This subject will be one of our future work topics. For each training instance (a pair of source sentence and target U-tree structure), the extracted GHKM minimal translation rules compose a unique STSG derivation9. Moreover, all the rules developed from the training data constitute an initial STSG for the Gibbs sampler.

Motivation

acl_arc_intent_classification:train:173

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: They use a Bag of Visual Words (BoVW) model (Lowe, 2004) to create a bimodal vocabulary describing documents. The first work to do this with topic models is Feng and Lapata (2010b). As computer vision techniques have improved over the past decade, other research has begun directly using visual information in place of feature norms. Citation Sentence: They use a Bag of Visual Words ( BoVW ) model ( Lowe , 2004 ) to create a bimodal vocabulary describing documents . Context after the citation: The topic model using the bimodal vocabulary outperforms a purely textual based model in word association and word similarity prediction. Bruni et al. (2012a) show how a BoVW model may be easily combined with a distributional vector space model of language using only vector concatenation. Bruni et al. (2012b) show that the contextual visual words (i.e. the visual features around an object, rather than of the object itself) are even more useful at times, suggesting the plausibility of a sort of distributional hypothesis for images. More recently, Silberer et al. (2013) show that visual attribute classifiers, which have been immensely successful in object recognition (Farhadi et al., 2009), act as excellent substitutes for feature

Background

acl_arc_intent_classification:train:174

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: W. Labov (1973) discussed sentences of the form *This is a chair but you can sit on it. In either formalization we get BUT_Cl as a consequence: Since &quot;laws&quot; cannot be deleted, BUT can't be applied, and hence its use in those kinds of sentences would be incorrect. If we subscribe to a more realistic view where definitions are given by a collection of central/prototypical and peripheral conditions, only the peripheral ones can be contradicted by &quot;but.&quot; Citation Sentence: W. Labov ( 1973 ) discussed sentences of the form * This is a chair but you can sit on it . Context after the citation: The sentence is incorrect, since the function &quot;one can sit on it&quot; belongs to the core of the concept &quot;chair&quot;; so—contrary to the role of &quot;but&quot;—the sentence does not contain any surprising new elements. Using the Metarule (BUT) and the cooperative principle of Grice, we get BUT_C2: 4) but IF is incorrect, if 4) ---4 kIf is a &quot;law.&quot; The Metarule (BUT) gives the semantics of &quot;but;&quot; the rules BUT_Cl and BUT_C2 follow from it (after formalization in a sufficiently strong metalanguage such as type theory or set theory). We can link all of them to procedures for constructing and evaluating models of text.

Background

acl_arc_intent_classification:train:175

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: This imbalance foils thresholding strategies, clever as they might be (Gale & Church, 1991; Wu & Xia, 1994; Chen, 1996). The correct translations of a word that has several correct translations will be assigned a lower probability than the correct translation of a word that has only one correct translation. Each word is assigned the same unit of probability mass, which the model distributes over all candidate translations. Citation Sentence: This imbalance foils thresholding strategies , clever as they might be ( Gale & Church , 1991 ; Wu & Xia , 1994 ; Chen , 1996 ) . Context after the citation: The likelihoods in the word-to-word model remain unnormalized, so they do not compete. The word-to-word model maintains high precision even given much less training data. Resnik & Melamed (1997) report that the model produced translation lexicons with 94% precision and 30% recall, when trained on French/English software manuals totaling about 400,000 words.

Background

acl_arc_intent_classification:train:176

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: The problem of handling ill-formed input has been studied by Carbonell and Hayes (1983), Granger (1983), Jensen et al. (1983), Kwasny and Sondheimer (1981), Riesbeck and Schank (1976), Thompson (1980), Weischedel and Black (1980), and Weischedel and Sondheimer (1983). A detailed description of the kinds of expectation mechanisms appearing in these systems appears in Fink (1983). While some of these systems did exhibit expectation capabilities at the sentence level, none acquired dialogues of the kind described here for the sake of dialogue level expectation and error correction. Citation Sentence: The problem of handling ill-formed input has been studied by Carbonell and Hayes ( 1983 ) , Granger ( 1983 ) , Jensen et al. ( 1983 ) , Kwasny and Sondheimer ( 1981 ) , Riesbeck and Schank ( 1976 ) , Thompson ( 1980 ) , Weischedel and Black ( 1980 ) , and Weischedel and Sondheimer ( 1983 ) . Context after the citation: A wide variety of techniques have been developed for addressing problems at the word, phrase, sentence, and in some cases, dialogue level. However, these methodologies have not used historical information at the dialogue level as described here. In most cases, the goal of these systems is to characterize the ill-formed input into classes of errors and to correct on that basis. The work described here makes no attempt to classify the errors, but treats them as random events that occur at any point in a sentence.

CompareOrContrast

acl_arc_intent_classification:train:177

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: Prototypes of Internet search engines for linguists, corpus linguists and lexicographers have been proposed: WebCorp (Kehoe and Renouf, 2002), KWiCFinder (Fletcher, 2004a) and the Linguist’s Search Engine (Kilgarriff, 2003; Resnik and Elkiss, 2003). Baroni and Bernardini (2004) built a corpus by iteratively searching Google for a small set of seed terms. Turney (2001) extracts word co-occurrence probabilities from unlabelled text collected from a web crawler. Citation Sentence: Prototypes of Internet search engines for linguists , corpus linguists and lexicographers have been proposed : WebCorp ( Kehoe and Renouf , 2002 ) , KWiCFinder ( Fletcher , 2004a ) and the Linguist 's Search Engine ( Kilgarriff , 2003 ; Resnik and Elkiss , 2003 ) . Context after the citation: A key concern in corpus linguistics and related disciplines is verifiability and replicability of the results of studies. Word frequency counts in internet search engines are inconsistent and unreliable (Veronis, 2005). Tools based on static corpora do not suffer from this problem, e.g. BNCweb7, developed at the University of Zurich, and View 8 (Variation in English Words and Phrases, developed at Brigham Young University) 4 http://www.comp.lancs.ac.uk/ucrel/claws/trial.html 5 http://www.comp.leeds.ac.uk/amalgam/amalgam/ amalghome.htm 6 http://www.connexor.com 7 http://homepage.mac.com/bncweb/home.html 8 http://view.byu.edu/

Background

acl_arc_intent_classification:train:178

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: In most recent research, NEs (person, location and organisations) are extracted from the text and used as a source of evidence to calculate the similarity between documents -see for instance (Blume, 2005; Chen and Martin, 2007; Popescu and Magnini, 2007; Kalashnikov et al., 2007). Ravin (1999) introduced a rule-based approach that tackles both variation and ambiguity analysing the structure of names. The most used feature for the Web People Search task, however, are NEs. Citation Sentence: In most recent research , NEs ( person , location and organisations ) are extracted from the text and used as a source of evidence to calculate the similarity between documents - see for instance ( Blume , 2005 ; Chen and Martin , 2007 ; Popescu and Magnini , 2007 ; Kalashnikov et al. , 2007 ) . Context after the citation: For instance, Blume (2005) uses NEs coocurring with the ambiguous mentions of a name as a key feature for the disambiguation process. Saggion (2008) compared the performace of NEs versus BoW features. In his experiments a only a representation based on Organisation NEs outperformed the word based approach. Furthermore, this result is highly dependent on the choice of metric weighting (NEs achieve high precision at the cost of a low recall and viceversa for BoW).

Background

acl_arc_intent_classification:train:179

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: The recent great advances in speech and language technologies have made it possible to build fully implemented spoken dialogue systems (Aust et al., 1995; Allen et al., 1996; Zue et al., 2000; Walker et al., 2000). Citation Sentence: The recent great advances in speech and language technologies have made it possible to build fully implemented spoken dialogue systems ( Aust et al. , 1995 ; Allen et al. , 1996 ; Zue et al. , 2000 ; Walker et al. , 2000 ) . Context after the citation: One of the next research goals is to make these systems task-portable, that is, to simplify the process of porting to another task domain. To this end, several toolkits for building spoken dialogue systems have been developed (Barnett and Singh, 1997; Sasajima et al., 1999). One is the CSLU Toolkit (Sutton et al., 1998), which enables rapid prototyping of a spoken dialogue system that incorporates a finite-state dialogue model. It decreases the amount of the effort required in building a spoken dialogue system in a user-defined task domain.

Background

acl_arc_intent_classification:train:18

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: More recently, Silberer et al. (2013) show that visual attribute classifiers, which have been immensely successful in object recognition (Farhadi et al., 2009), act as excellent substitutes for feature Bruni et al. (2012b) show that the contextual visual words (i.e. the visual features around an object, rather than of the object itself) are even more useful at times, suggesting the plausibility of a sort of distributional hypothesis for images. Bruni et al. (2012a) show how a BoVW model may be easily combined with a distributional vector space model of language using only vector concatenation. Citation Sentence: More recently , Silberer et al. ( 2013 ) show that visual attribute classifiers , which have been immensely successful in object recognition ( Farhadi et al. , 2009 ) , act as excellent substitutes for feature Context after the citation: norms. Other work on modeling the meanings of verbs using video recognition has also begun showing great promise (Mathe et al., 2008; Regneri et al., 2013). The Computer Vision community has also benefited greatly from efforts to unify the two modalities. To name a few examples, Rohrbach et al. (2010) and Socher et al. (2013) show how semantic information from text can be used to improve zero-shot classification (i.e., classifying never-before-seen objects), and Motwani and Mooney (2012) show that verb clusters can be used to improve activity recognition in videos.

Background

acl_arc_intent_classification:train:180

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: In other methods, lexical resources are specifically tailored to meet the requirements of the domain (Rosario and Hearst, 2001) or the system (Gomez, 1998). Some methods of semantic relation analysis rely on predefined templates filled with information from processed texts (Baker et al., 1998). Citation Sentence: In other methods , lexical resources are specifically tailored to meet the requirements of the domain ( Rosario and Hearst , 2001 ) or the system ( Gomez , 1998 ) . Context after the citation: Such systems extract information from some types of syntactic units (clauses in (Fillmore and Atkins, 1998; Gildea and Jurafsky, 2002; Hull and Gomez, 1996); noun phrases in (Hull and Gomez, 1996; Rosario et al., 2002)). Lists of semantic relations are designed to capture salient domain information. In the Rapid Knowledge Formation Project (RKF) a support system was developed for domain experts. It helps them build complex knowledge bases by combining components: events, entities and modifiers (Clark and Porter, 1997).

Background

acl_arc_intent_classification:train:181

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: Such systems extract information from some types of syntactic units (clauses in (Fillmore and Atkins, 1998; Gildea and Jurafsky, 2002; Hull and Gomez, 1996); noun phrases in (Hull and Gomez, 1996; Rosario et al., 2002)). In other methods, lexical resources are specifically tailored to meet the requirements of the domain (Rosario and Hearst, 2001) or the system (Gomez, 1998). Some methods of semantic relation analysis rely on predefined templates filled with information from processed texts (Baker et al., 1998). Citation Sentence: Such systems extract information from some types of syntactic units ( clauses in ( Fillmore and Atkins , 1998 ; Gildea and Jurafsky , 2002 ; Hull and Gomez , 1996 ) ; noun phrases in ( Hull and Gomez , 1996 ; Rosario et al. , 2002 ) ) . Context after the citation: Lists of semantic relations are designed to capture salient domain information. In the Rapid Knowledge Formation Project (RKF) a support system was developed for domain experts. It helps them build complex knowledge bases by combining components: events, entities and modifiers (Clark and Porter, 1997). The system’s interface facilitates the expert’s task of creating and manipulating structures which represent domain concepts, and assigning them relations from a relation dictionary.

Background

acl_arc_intent_classification:train:182

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: The third version (VOYAGER) serves as an interface both with a recognizer and with a functioning database back-end (Zue et al. 1990). The second version (RM) concerns the Resource Management task (Pallett 1989) that has been popular within the DARPA community in recent years. The first version (TIMIT) was developed for the 450 phonetically rich sentences of the TIMIT database (Lamel et al. 1986). Citation Sentence: The third version ( VOYAGER ) serves as an interface both with a recognizer and with a functioning database back-end ( Zue et al. 1990 ) . Context after the citation: The VOYAGER system can answer a number of different types of questions concerning navigation within a city, as well as provide certain information about hotels, restaurants, libraries, etc., within the region. A fourth domain-specific version is under development for the ATIS (Air Travel Information System) task, which has recently been designated as the new common task for the DARPA community.

Uses

acl_arc_intent_classification:train:183

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: We use the non-projective k-best MST algorithm to generate k-best lists (Hall, 2007), where k = 8 for the experiments in this paper. based parsing algorithms with an arc-factored parameterization (McDonald et al., 2005). • Graph-based: An implementation of graph- Citation Sentence: We use the non-projective k-best MST algorithm to generate k-best lists ( Hall , 2007 ) , where k = 8 for the experiments in this paper . Context after the citation: The graphbased parser features used in the experiments in this paper are defined over a word, wi at position i; the head of this word wρ(i) where ρ(i) provides the index of the head word; and partof-speech tags of these words ti. We use the following set of features similar to McDonald et al. (2005):

Uses

acl_arc_intent_classification:train:184

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: As for work on Arabic (MSA), results have been reported on the PATB (Kulick, Gabbard, and Marcus 2006; Diab 2007; Green and Manning 2010), the Prague Dependency Treebank (PADT) (Buchholz and Marsi 2006; Nivre 2008) and the CATiB (Habash and Roth 2009). Looking at Hebrew, a Semitic language related to Arabic, Tsarfaty and Sima’an (2007) report that extending POS and phrase structure tags with definiteness information helps unlexicalized PCFG parsing. We also find that the number feature helps for Arabic. Citation Sentence: As for work on Arabic ( MSA ) , results have been reported on the PATB ( Kulick , Gabbard , and Marcus 2006 ; Diab 2007 ; Green and Manning 2010 ) , the Prague Dependency Treebank ( PADT ) ( Buchholz and Marsi 2006 ; Nivre 2008 ) and the CATiB ( Habash and Roth 2009 ) . Context after the citation: Recently, Green and Manning (2010) analyzed the PATB for annotation consistency, and introduced an enhanced split-state constituency grammar, including labels for short idafa constructions and verbal or equational clauses. Nivre (2008) reports experiments on Arabic parsing using his MaltParser (Nivre et al. 2007), trained on the PADT. His results are not directly comparable to ours because of the different treebank representations, even though all the experiments reported here were performed using the MaltParser. Our results agree with previous work on Arabic and Hebrew in that marking the definite article is helpful for parsing.

Background

acl_arc_intent_classification:train:185

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: For example, McKnight and Srinivasan (2003) describe a machine learning approach to automatically label sentences as belonging to introduction, methods, results, or conclusion using structured abstracts as training data (see also Lin et al. 2006). The literature also contains work on sentence-level classification of MEDLINE abstracts for non-clinical purposes. 8 Although note that answer generation from the PubMed results also requires the use of the outcome extractor. Citation Sentence: For example , McKnight and Srinivasan ( 2003 ) describe a machine learning approach to automatically label sentences as belonging to introduction , methods , results , or conclusion using structured abstracts as training data ( see also Lin et al. 2006 ) . Context after the citation: Tbahriti et al. (2006) have demonstrated that differential weighting of automatically labeled sections can lead to improved retrieval performance. Note, however, that such labels are orthogonal to PICO frame elements, and hence are not directly relevant to knowledge extraction for clinical question answering. In a similar vein, Light, Qiu, and Srinivasan (2004) report on the identification of speculative statements in MEDLINE abstracts, but once again, this work is not directly applicable to clinical question answering. In addition to question answering, multi-document summarization provides a complementary approach to addressing clinical information needs.

Background

acl_arc_intent_classification:train:186

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: • Graph transformations for recovering nonprojective structures (Nivre and Nilsson, 2005). • Support vector machines for mapping histories to parser actions (Kudo and Matsumoto, 2002). • History-based feature models for predicting the next parser action (Black et al., 1992). Citation Sentence: • Graph transformations for recovering nonprojective structures ( Nivre and Nilsson , 2005 ) . Context after the citation: All experiments have been performed using MaltParser (Nivre et al., 2006), version 0.4, which is made available together with the suite of programs used for preand post-processing.1 1www. msi.vxu.se/users/nivre/research/MaltParser.html

Uses

acl_arc_intent_classification:train:187

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: Klein and Manning (2002)’s CCM is an unlabeled bracketing model that generates the span of part-of-speech tags that make up each constituent and the pair of tags surrounding each constituent span (as well as the spans and contexts of each non-constituent). Citation Sentence: Klein and Manning ( 2002 ) 's CCM is an unlabeled bracketing model that generates the span of part-of-speech tags that make up each constituent and the pair of tags surrounding each constituent span ( as well as the spans and contexts of each non-constituent ) . Context after the citation: They found that modeling constituent context aids in parser learning because it is able to capture the observation that the same contexts tend to appear repeatedly in a corpus, even with different constituents. While CCM is designed to learn which tag pairs make for likely contexts, without regard for the constituents themselves, our model attempts to learn the relationships between context categories and the types of the constituents, allowing us to take advantage of the natural a priori knowledge about which contexts fit with which constituent labels. Other researchers have shown positive results for grammar induction by introducing relatively small amounts of linguistic knowledge. Naseem et al. (2010) induced dependency parsers by handconstructing a small set of linguistically-universal dependency rules and using them as soft constraints during learning.

Background

acl_arc_intent_classification:train:188

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: This process produces a hierarchical clustering of the word types in the corpus, and these clusterings have been found useful in many applications (Ratinov and Roth, 2009; Koo et al., 2008; Miller et al., 2004). The Brown clustering algorithm works by starting with an initial assignment of word types to classes (which is usually either one unique class per type or a small number of seed classes corresponding to the most frequent types in the corpus), and then iteratively selecting the pair of classes to merge that would lead to the highest post-merge log-likelihood, doing so until all classes have been merged. Given a one-toone assignment of word types to classes, then, and a corpus of text, it is easy to estimate these probabilities with maximum likelihood by counting the frequencies of the different class bigrams and the frequencies of word tokens of each type in the corpus. Citation Sentence: This process produces a hierarchical clustering of the word types in the corpus , and these clusterings have been found useful in many applications ( Ratinov and Roth , 2009 ; Koo et al. , 2008 ; Miller et al. , 2004 ) . Context after the citation: There are other similar models of distributional clustering of English words which can be similarly effective (Pereira et al., 1993). One limitation of Brown clusters is their computational complexity, as training takes O(kV 2 + N)x time to train, where k is the number of base clusters, V size of vocabulary, and N number of tokens. This is infeasible for large corpora with millions of word types. Another family of language models that produces embeddings is the neural language models.

Motivation

acl_arc_intent_classification:train:189

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: We posit that this would not have a significant effect on the results, in particular for MML-based classification techniques, such as Decision Graphs (Oliver 1993). 16 In principle, we could have used a classification method to predict clusters from the values of the confidence measures for unseen cases. The standard approach for combining precision and recall is to compute their harmonic mean, F-score, as we have done in our Citation Sentence: We posit that this would not have a significant effect on the results , in particular for MML-based classification techniques , such as Decision Graphs ( Oliver 1993 ) . Context after the citation: comparative evaluation in Section 4. However, in order to accommodate different levels of preference towards precision or recall, as discussed herein, we use the following weighted F-score calculation (van Rijsbergen 1979). where w is a weight between 0 and 1 given to precision. When w = 0.5 we have the standard usage of F-score (Equation (9)), and for w > 0.5, we have a preference for high precision.

Background

acl_arc_intent_classification:train:19

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Opposition (called &quot;adversative&quot; or &quot;contrary-to-expectation&quot; by Halliday and Hasan 1976; cfXXX also Quirk et al. 1972, p. 672). 1. However, &quot;but&quot; does not behave quite like the other two—semantically, &quot;but&quot; signals a contradiction, and in this role it seems to have three subfunctions: Citation Sentence: Opposition ( called `` adversative '' or `` contrary-to-expectation '' by Halliday and Hasan 1976 ; cfXXX also Quirk et al. 1972 , p. 672 ) . Context after the citation: The ship arrived but the passengers could not get off. The yacht is cheap but elegant. 2. Comparison.

Background

acl_arc_intent_classification:train:190

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Aside from the extraction of theory-neutral subcategorization lexicons, there has also been work in the automatic construction of lexical resources which comply with the principles of particular linguistic theories such as LTAG, CCG, and HPSG (Chen and Vijay-Shanker 2000; Xia 1999; Hockenmaier, Bierner, and Baldridge 2004; Nakanishi, Miyao, and Tsujii 2004). Given these facts, research on automating acquisition of dictionaries for lexically based NLP systems is a particularly important issue. Manning (1993) argues that, aside from missing domain-specific complementation trends, dictionaries produced by hand will tend to lag behind real language use because of their static nature. Citation Sentence: Aside from the extraction of theory-neutral subcategorization lexicons , there has also been work in the automatic construction of lexical resources which comply with the principles of particular linguistic theories such as LTAG , CCG , and HPSG ( Chen and Vijay-Shanker 2000 ; Xia 1999 ; Hockenmaier , Bierner , and Baldridge 2004 ; Nakanishi , Miyao , and Tsujii 2004 ) . Context after the citation: In this article we present an approach to automating the process of lexical acquisition for LFG (i.e., grammatical-function-based systems). However, our approach also generalizes to CFG category-based approaches. In LFG, subcategorization requirements are enforced through semantic forms specifying which grammatical functions are required by a particular predicate. Our approach is based on earlier work on LFG semantic form extraction (van Genabith, Sadler, and Way 1999) and recent progress in automatically annotating the Penn-II and Penn-III Treebanks with LFG f-structures (Cahill et al. 2002; Cahill, McCarthy, et al. 2004).

Background

acl_arc_intent_classification:train:191

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: 1 The representation in Mohri and Pereira (1998) is even more compact than ours for grammars that are not self-embedding. This means that new e-transitions connect q to the start state of A, and the final states of A, to q'. Replace each transition in Ap of the form (q, A„ q') by (a copy of) automaton A, in a straightforward way. Citation Sentence: 1 The representation in Mohri and Pereira ( 1998 ) is even more compact than ours for grammars that are not self-embedding . Context after the citation: However, in this paper we use our representation as an intermediate result in approximating an unrestricted context-free grammar, with the final objective of obtaining a single minimal deterministic automaton. For this purpose, Mohri and Pereira's representation offers little advantage. Nederhof Experiments with Regular Approximation 3.

CompareOrContrast

acl_arc_intent_classification:train:192

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: 29 This improvement of the covariation encoding can also be viewed as an instance of the program transformation technique referred to as deletion of clauses with a finitely failed body (Pettorossi and Proietti 1994). As a result, the literal can be removed from the body of Intuitively understood, unfolding comprises the evaluation of a particular literal in the body of a clause at compile-time. Citation Sentence: 29 This improvement of the covariation encoding can also be viewed as an instance of the program transformation technique referred to as deletion of clauses with a finitely failed body ( Pettorossi and Proietti 1994 ) . Context after the citation: the clause. Whereas unfolding can be viewed as a symbolic way of going forward in computation, folding constitutes a symbolic step backwards in computation. Given a lexical entry as in Figure 15, we can discard all frame clauses that presuppose ti as the value of c, as discussed in the previous section. To eliminate the frame predicates completely, we can successively unfold the frame predicates and the lexical rule predicates with respect to the interaction predicates.'

CompareOrContrast

acl_arc_intent_classification:train:193

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: The reordering models we describe follow our previous work using function word models for translation (Setiawan et al., 2007; Setiawan et al., 2009). Citation Sentence: The reordering models we describe follow our previous work using function word models for translation ( Setiawan et al. , 2007 ; Setiawan et al. , 2009 ) . Context after the citation: The core hypothesis in this work is that function words provide robust clues to the reordering patterns of the phrases surrounding them. To make this insight useful for alignment, we develop features that score the alignment configuration of the neighboring phrases of a function word (which functions as an anchor) using two kinds of information: 1) the relative ordering of the phrases with respect to the function word anchor; and 2) the span of the phrases. This section provides a high level overview of our reordering model, which attempts to leverage this information. To facilitate subsequent discussions, we introduce the notion of monolingual function word phrase FWi, which consists of the tuple (Yi, Li, Ri), where Yi is the i-th function word and Li,Ri are its left and right neighboring phrases, respectively.

Extends

acl_arc_intent_classification:train:194

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Although there are other discussions of the paragraph as a central element of discourse (e.g. Chafe 1979, Halliday and Hasan 1976, Longacre 1979, Haberlandt et al. 1980), all of them share a certain limitation in their formal techniques for analyzing paragraph structure. Citation Sentence: Although there are other discussions of the paragraph as a central element of discourse ( e.g. Chafe 1979 , Halliday and Hasan 1976 , Longacre 1979 , Haberlandt et al. 1980 ) , all of them share a certain limitation in their formal techniques for analyzing paragraph structure . Context after the citation: Discourse linguists show little interest in making the structural descriptions precise enough so that a computational grammar of text could adapt them and use them. Our interest, however, lies precisely in that area. We suggest that the paragraph is a grammatical and logical unit. It is the smallest linguistic representation of what, in logic, is called a &quot;model,&quot; and it is the first reasonable domain of anaphora resolution, and of coherent thought about a central topic.

CompareOrContrast

acl_arc_intent_classification:train:195

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: For example, such schema can serve as a mean to represent translation examples, or find structural correspondences for the purpose of transfer grammar learning (Menezes & Richardson, 2001), (Aramaki et al., 2001), (Watanabe et al., 2000), (Meyers et al., 2000), (Matsumoto et al., 1993), (kaji et al., 1992), and example-base machine translation EBMT3 (Sato & Nagao, 1990), (Sato, 1991), (Richardson et al., 2001), (Al-Adhaileh & Tang, 1999). Due to these limitations, instead of investigating into the synchronization of two grammars, we propose a flexible annotation schema (i.e. Synchronous Structured String-Tree Correspondence (S-SSTC)) to realize additional power and flexibility in expressing structural correspondences at the level of language sentence pairs. Similar limitations also appear in synchronous CFGs (Harbusch & Poller,1994). Citation Sentence: For example , such schema can serve as a mean to represent translation examples , or find structural correspondences for the purpose of transfer grammar learning ( Menezes & Richardson , 2001 ) , ( Aramaki et al. , 2001 ) , ( Watanabe et al. , 2000 ) , ( Meyers et al. , 2000 ) , ( Matsumoto et al. , 1993 ) , ( kaji et al. , 1992 ) , and example-base machine translation EBMT3 ( Sato & Nagao , 1990 ) , ( Sato , 1991 ) , ( Richardson et al. , 2001 ) , ( Al-Adhaileh & Tang , 1999 ) . Context after the citation:

Background

acl_arc_intent_classification:train:196

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: In corpus linguistics building such megacorpora is beyond the scope of individual researchers, and they are not easily accessible (Kennedy, 1998: 56) unless the web is used as a corpus (Kilgarriff and Grefenstette, 2003). Due to the Zipfian nature of word frequencies, around half the word types in a corpus occur only once, so tremendous increases in corpus size are required both to ensure inclusion of essential word and phrase types and to increase the chances of multiple occurrences of a given type. The motivation for increasingly large data sets remains the same. Citation Sentence: In corpus linguistics building such megacorpora is beyond the scope of individual researchers , and they are not easily accessible ( Kennedy , 1998 : 56 ) unless the web is used as a corpus ( Kilgarriff and Grefenstette , 2003 ) . Context after the citation: Increasingly, corpus researchers are tapping the Web to overcome the sparse data problem (Keller et al., 2002). This topic generated intense interest at workshops held at the University of Heidelberg (October 2004), University of Bologna (January 2005), University of Birmingham (July 2005) and now in Trento in April 2006. In addition, the advantages of using linguistically annotated data over raw data are well documented (Mair, 2005; Granger and Rayson, 1998). As the size of a corpus increases, a near linear increase in computing power is required to annotate the text.

Background

acl_arc_intent_classification:train:197

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: Here 11 is an optimization precision, oc is a step size chosen with the strong Wolfe’s rule (Nocedal and Wright 1999). We optimize the dual objective using the gradient based methods shown in Algorithm 1. Hence the projection step uses the same inference algorithm (forward–backward for HMMs) to compute the gradient, only modifying the local factors using the current setting of λ. Citation Sentence: Here 11 is an optimization precision , oc is a step size chosen with the strong Wolfe 's rule ( Nocedal and Wright 1999 ) . Context after the citation: Here, PV(A) represents an ascent direction chosen as follows: For inequality constraints, it is the projected gradient (Bertsekas 1999); for equality constraints with slack, we use conjugate gradient (Nocedal and Wright 1999), noting that when A = 0, the objective is not differentiable. In practice this only happens at the start of optimization and we use a sub-gradient for the first direction. Computing the projection requires an algorithm for inference in the original model, and uses that inference as a subroutine. For HMM word alignments, we need to make several calls to forward–backward in order to choose A. Setting the optimization precision 11 more loosely allows the optimization to terminate more quickly but at a less accurate value.

Uses

acl_arc_intent_classification:train:198

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: See (Gomez, 1998) for a discussion. The number and nature of the thematic roles depend on the generic predicates and subpredicates, and not on some general criteria regardless of each predicate (Fillmore, 1968). Subpredicates inherit all the thematic roles not listed in their definitions from their parent predicates. Citation Sentence: See ( Gomez , 1998 ) for a discussion . Context after the citation:

Background

acl_arc_intent_classification:train:199