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they showed that the average agreement among the human judges is .76, and the average agreement between ST(system) and the humans is .75, or about 99% of the interhuman agreement. | 0 | Thus, rather than give a single evaluative score, we prefer to compare the performance of our method with the judgments of several human subjects. |
The AdaBoost algorithm was developed for supervised learning. | 0 | (6), with W+ > W_. |
The resulting model is compact, efficiently learnable and linguistically expressive. | 0 | They are set to fixed constants. |
Bean and Riloff also used bootstrapping to extend their semantic compatibility model, proposed using caseframe network for anaphora resolution, information extraction patterns to identify contextual clues for determining compatibility between NPs. | 1 | 2.2.2 The Caseframe Network The first type of contextual role knowledge that BABAR learns is the Caseframe Network (CFNet), which identifies caseframes that co-occur in anaphor/antecedent resolutions. |
Koehn and Monz carried out an extensive manual and automatic evaluation of machine translation performance on European language pairs. | 0 | It was our hope that this competition, which included the manual and automatic evaluation of statistical systems and one rulebased commercial system, will give further insight into the relation between automatic and manual evaluation. |
There is no global pruning. | 0 | This measure has the advantage of being completely automatic. |
Most IE researchers have been creating paraphrase knowledge by hand and specific tasks. | 0 | We will report the evaluation results in the next subsection. |
The overall performance of their joint framework demonstrated that a probability distribution obtained over mere syntactic contexts using a Treebank grammar and a data-driven lexicon outperformed upper bounds proposed by previous joint disambiguation systems and achieved segmentation and parsing results on a par with state-of-the-art standalone applications results. | 0 | Both (Tsarfaty, 2006; Cohen and Smith, 2007) have shown that a single integrated framework outperforms a completely streamlined implementation, yet neither has shown a single generative model which handles both tasks. |
Using a treebank grammar, the data-driven lexicon, and a linguistically motivated unknown-tokens handling technique their model outperformed previous pipelined, integrated or factorized systems for Hebrew morphological and syntactic processing, yielding an error reduction of 12% over the best published results so far. | 0 | While the linear precedence of segmental morphemes within a token is subject to constraints, the dominance relations among their mother and sister constituents is rather free. |
It also incorporates the Good-Turing methodin estimating the likelihoods of previously unseen constructions, including morphological derivatives and personal names. | 0 | set was based on an earlier version of the Chang et a!. |
On the basis of that observation, they described a class of formalisms which they called Linear Context- Free Rewriting Systems (LCFRs), and showed they were recognizable in polynomial time and generated only semilinear languages. | 0 | The tee pumping lemma states that if there is tree, t = 22 t2t3, generated by a CFG G, whose height is more than a predetermined bound k, then all trees of the form ti tP3 for each i > 0 will also generated by G (as shown in Figure 9b). |
The code is open source, has minimal dependencies, and offers both C++ and Java interfaces for integration. | 0 | Chris Dyer integrated the code into cdec. |
Replacing this with a ranked evaluation seems to be more suitable. | 0 | Human judges also pointed out difficulties with the evaluation of long sentences. |
Other kinds of productive word classes, such as company names, abbreviations,and place names can easily be handled given appropriate models. | 0 | However, there will remain a large number of words that are not readily adduced to any produc tive pattern and that would simply have to be added to the dictionary. |
Other kinds of productive word classes, such as company names, abbreviations,and place names can easily be handled given appropriate models. | 0 | Methods for expanding the dictionary include, of course, morphological rules, rules for segmenting personal names, as well as numeral sequences, expressions for dates, and so forth (Chen and Liu 1992; Wang, Li, and Chang 1992; Chang and Chen 1993; Nie, Jin, and Hannan 1994). |
This paper talks about Pseudo-Projective Dependency Parsing. | 0 | In section 4 we evaluate these transformations with respect to projectivized dependency treebanks, and in section 5 they are applied to parser output. |
Here both parametric and non-parametric models are explored. | 0 | We model each parse as the decisions made to create it, and model those decisions as independent events. |
The three parsers were trained and tuned by their creators on various sections of the WSJ portion of the Penn Treebank. | 0 | The substructures that are unanimously hypothesized by the parsers should be preserved after combination, and the combination technique should not foolishly create substructures for which there is no supporting evidence. |
They focused on phrases which two Named Entities, and proceed in two stages. | 0 | Also, in Information Extraction (IE), in which the system tries to extract elements of some events (e.g. date and company names of a corporate merger event), several event instances from different news articles have to be aligned even if these are expressed differently. |
In order to realize the full potential of dependency-based syntactic parsing, it is desirable to allow non-projective dependency structures. | 0 | Table 2 shows the features used in the current version of the parser. |
In this paper, the authors proposed an approach for instance-weighting phrase pairs in an out-of-domain corpus in order to improve in-domain performance. | 0 | We have already mentioned the closely related work by Matsoukas et al (2009) on discriminative corpus weighting, and Jiang and Zhai (2007) on (nondiscriminative) instance weighting. |
The model incorporates various recent techniques for incorporating and manipulating linguistic knowledge using finite-state transducers. | 0 | For example, given a sequence F1G1G2, where F1 is a legal single-hanzi family name, and Plural Nouns X g 0 g "' X X 0 T!i c"'. |
The features were weighted within a logistic model that gave an overall weight that was applied to the phrase pair and MAP-smoothed relative-frequency estimates which were combined linearly with relative-frequency estimates from an in-domain phrase table. | 0 | We have not explored this strategy. |
The approach has been successfully tested on the 8 000-word Verbmobil task. | 0 | (1), Pr(eI 1) is the language model, which is a trigram language model in this case. |
From a DP-based solution to the traveling salesman problem, they present a novel technique to restrict the possible word reordering between source and target language in order to achieve an eÃcient search algorithm. | 0 | The search starts in hypothesis (f;g; 0) and ends in the hypotheses (f1; ; Jg; j), with j 2 f1; ; Jg. |
The bias of automatic methods in favour of statistical systems seems to be less pronounced on out-of-domain test data. | 0 | The test data was again drawn from a segment of the Europarl corpus from the fourth quarter of 2000, which is excluded from the training data. |
The evaluation compares the performance of the system with that of several human judges and inter-human agreement on a single correct way to segment a text. | 0 | For instance, the common "suffixes," -nia (e.g.,. |
In this paper the authors present a stochastic finite-state model for segmenting Chinese text into words. | 0 | One class comprises words derived by productive morphologi cal processes, such as plural noun formation using the suffix ir, menD. |
Here we show how non-projective dependency parsing can be achieved by combining a data driven projective parser with special graph transformation techniques. | 0 | Even this may be nondeterministic, in case the graph contains several non-projective arcs whose lifts interact, but we use the following algorithm to construct a minimal projective transformation D0 = (W, A0) of a (nonprojective) dependency graph D = (W, A): The function SMALLEST-NONP-ARC returns the non-projective arc with the shortest distance from head to dependent (breaking ties from left to right). |
It is annotated with several data: morphology, syntax, rhetorical structure, connectors, correference and informative structure. | 0 | (Carlson, Marcu 2001) responded to this situation with relatively precise (and therefore long!) |
Their empirical results demonstrate that the type-based tagger rivals state-of-the-art tag-level taggers which employ more sophisticated learning mechanisms to exploit similar constraints. | 0 | 8 1 8. |
Throughout this paper we compare with several packages: SRILM 1.5.12 (Stolcke, 2002) is a popular toolkit based on tries used in several decoders. | 0 | We run the baseline Moses system for the French-English track of the 2011 Workshop on Machine Translation,9 translating the 3003-sentence test set. |
We chose one of four labels for each example: location, person, organization, or noise where the noise category was used for items that were outside the three categories. | 0 | The problem is a binary classification problem. |
They showed that it was useful to abstract away from the details of the formalism, and examine the nature of their derivation process as reflected by properties their trees, find that several of the formalisms considered can be seen as being closely related since they have derivation tree sets with the same structure as those produced by Context-Free Grammars. | 0 | A geometrical progression of language families defined by Weir (1987) involves tree sets with increasingly complex path sets. |
However, these approaches are ill-equipped to directly represent type-based constraints such as sparsity. | 0 | For other languages, we use the CoNLL-X multilingual dependency parsing shared task corpora (Buchholz and Marsi, 2006) which include gold POS tags (used for evaluation). |
NER is useful in many NLP applications such as information extraction, question answering, etc .Chieu and Ng have shown that the maximum entropy framework is able to use global information directly from various sources. | 0 | Previous work that involves the gathering of information from the whole document often uses a secondary classifier, which corrects the mistakes of a primary sentence- based classifier. |
In this paper, the authors are of the opinion that the sequence models-based approaches usually treat token-level tag assignment as the primary latent variable. | 0 | While Berg-Kirkpatrick et al. |
Bean and Riloff also used bootstrapping to extend their semantic compatibility model, proposed using caseframe network for anaphora resolution, information extraction patterns to identify contextual clues for determining compatibility between NPs. | 0 | Terrorism systems must distinguish between people who perpetrate a crime and people who are victims of a crime. |
Their work is closely related to recent approaches that incorporate the sparsity constraint into the POS induction process. | 0 | 3 68.9 50. |
They focused on phrases which two Named Entities, and proceed in two stages. | 0 | In total, across all domains, we kept 13,976 phrases with keywords. |
It also incorporates the Good-Turing methodin estimating the likelihoods of previously unseen constructions, including morphological derivatives and personal names. | 0 | 4. |
For the perplexity and translation tasks, we used SRILM to build a 5-gram English language model on 834 million tokens from Europarl v6 (Koehn, 2005) and the 2011 Workshop on Machine Translation News Crawl corpus with duplicate lines removed. | 0 | Along with IRSTLM and TPT, our binary format is memory mapped, meaning the file and in-memory representation are the same. |
they showed that the average agreement among the human judges is .76, and the average agreement between ST(system) and the humans is .75, or about 99% of the interhuman agreement. | 0 | Papers that use this method or minor variants thereof include Liang (1986), Li et al. |
They employed a PCFG-based generative framework to make both syntactic and morphological disambiguation decisions which were not only theoretically clean and linguistically justified but also probabilistically appropriate and empirically sound. | 0 | In such cases we use the non-pruned lattice including all (possibly ungrammatical) segmentation, and let the statistics (including OOV) decide. |
The fact that projective dependency parsers can never exactly reproduce the analyses found in non-projective tree banks is often neglected because of the relative scarcity of problematic constructions. | 0 | However, it can be noted that the results for the least informative encoding, Path, are almost comparable, while the third encoding, Head, gives substantially worse results for both data sets. |
The approach has been successfully tested on the 8 000-word Verbmobil task. | 0 | 2. |
There is no global pruning. | 1 | However there is no global pruning. |
The texts were annotated with the RSTtool. | 0 | Hence we decided to select ten commentaries to form a âcore corpusâ, for which the entire range of annotation levels was realized, so that experiments with multi-level querying could commence. |
They proposed an unsupervised method to discover paraphrases from a large untagged corpus. | 0 | After tagging a large corpus with an automatic NE tagger, the method tries to find sets of paraphrases automatically without being given a seed phrase or any kinds of cue. |
They used graph-based label propagation for cross-lingual knowledge transfer and used the projected labels as features in an unsupervised model. | 0 | We used C = 1.0 as the L2 regularization constant in (Eq. |
This assumption, however, is not inherent to type-based tagging models. | 0 | While possible to utilize the feature-based log-linear approach described in Berg-Kirkpatrick et al. |
They have made use of local and global features to deal with the instances of same token in a document. | 0 | For each token , zero, one, or more of the features in each feature group are set to 1. |
Evaluation was done automatically using the BLEU score and manually on fluency and adequacy. | 0 | In words, the judgements are normalized, so that the average normalized judgement per judge is 3. |
Nevertheless, only a part of this corpus (10 texts), which the authors name "core corpus", is annotated with all this information. | 0 | Different annotations of the same text are mapped into the same data structure, so that search queries can be formulated across annotation levels. |
The authors use linguistic and annotation insights to develop a manually annotated grammar and evaluate it and finally provide a realistic evaluation in which segmentation is performed in a pipeline jointly with parsing. | 0 | Richer tag sets have been suggested for modeling morphologically complex distinctions (Diab, 2007), but we find that linguistically rich tag sets do not help parsing. |
Vijay-Shankar et all considered the structural descriptions produced by various grammatical formalisms in terms of the complexity of the paths and the relationship between paths in the sets of structural descriptions that each system can generate. | 0 | Head Grammars (HG's), introduced by Pollard (1984), is a formalism that manipulates headed strings: i.e., strings, one of whose symbols is distinguished as the head. |
While many systems had similar performance, the results offered interesting insights, especially, about the relative performance of statistical and rule-based systems. | 0 | If one system is better in 95% of the sample sets, we conclude that its higher BLEU score is statistically significantly better. |
The second algorithm builds on a boosting algorithm called AdaBoost. | 0 | Each xii is a member of X, where X is a set of possible features. |
We chose one of four labels for each example: location, person, organization, or noise where the noise category was used for items that were outside the three categories. | 0 | We also removed the context-type feature type when using the CoBoost approach. |
Bean and Riloff also used bootstrapping to extend their semantic compatibility model, proposed using caseframe network for anaphora resolution, information extraction patterns to identify contextual clues for determining compatibility between NPs. | 0 | BABAR employs information extraction techniques to represent and learn role relationships. |
Other kinds of productive word classes, such as company names, abbreviations,and place names can easily be handled given appropriate models. | 0 | In this paper we have argued that Chinese word segmentation can be modeled ef fectively using weighted finite-state transducers. |
The main result is that the combined system can recover non-projective dependencies with a precision sufficient to give a significant improvement in overall parsing accuracy, especially with respect to the exact match criterion, leading to the best reported performance for robust non-projective parsing of Czech. | 0 | The prediction based on these features is a knearest neighbor classification, using the IB1 algorithm and k = 5, the modified value difference metric (MVDM) and class voting with inverse distance weighting, as implemented in the TiMBL software package (Daelemans et al., 2003). |
These clusters are computed using an SVD variant without relying on transitional structure. | 0 | Instead, we condition on the type-level tag assignments T . Specifically, let St = {i|Ti = t} denote the indices of theword types which have been assigned tag t accord ing to the tag assignments T . Then θt is drawn from DIRICHLET(α, St), a symmetric Dirichlet which only places mass on word types indicated by St. This ensures that each word will only be assigned a single tag at inference time (see Section 4). |
The AdaBoost algorithm was developed for supervised learning. | 0 | (If fewer than n rules have Precision greater than pin, we 3Note that taking tlie top n most frequent rules already makes the method robut to low count events, hence we do not use smoothing, allowing low-count high-precision features to be chosen on later iterations. keep only those rules which exceed the precision threshold.) pm,n was fixed at 0.95 in all experiments in this paper. |
The authors in this paper describe a search procedure for statistical machine translation (MT) based on dynamic programming (DP). | 0 | The traveling salesman problem is an optimization problem which is defined as follows: given are a set of cities S = s1; ; sn and for each pair of cities si; sj the cost dij > 0 for traveling from city si to city sj . We are looking for the shortest tour visiting all cities exactly once while starting and ending in city s1. |
In this paper the authors present a stochastic finite-state model for segmenting Chinese text into words. | 0 | Similarly, there is no compelling evidence that either of the syllables of f.ifflll binllang2 'betelnut' represents a morpheme, since neither can occur in any context without the other: more likely fjfflll binllang2 is a disyllabic morpheme. |
They showed better grammars to improve performance on both morphological and syntactic tasks, providing support for the advantage of a joint framework over pipelined or factorized ones. | 0 | To control for the effect of the HSPELL-based pruning, we also experimented with a morphological analyzer that does not perform this pruning. |
Here we present two algorithms. | 0 | (Yarowsky 95) describes an algorithm for word-sense disambiguation that exploits redundancy in contextual features, and gives impressive performance. |
This architecture provides a uniform framework in which it is easy to incorporate not only listed dictionary entries but also morphological derivatives, and models for personal names and foreign names in transliteration. | 0 | (a) ;IE shi4 'be' => ;IE;IE shi4bu2-shi4 (be-not-be) 'is it?' |
The model incorporates various recent techniques for incorporating and manipulating linguistic knowledge using finite-state transducers. | 0 | immediately by a Romanization into the pinyin transliteration scheme; numerals following each pinyin syllable represent tones. |
They plan on extending instance-weighting to other standard SMT components and capture the degree of generality of phrase pairs. | 0 | We introduce several new ideas. |
The TRIE model uses less memory than the smallest lossless alternative and is still faster than SRILM. | 0 | We reduce this to O(log log |A|) time by evenly distributing keys over their range then using interpolation search4 (Perl et al., 1978). |
A large number of rules are needed for coverage of the domain, suggesting that a fairly large number of labeled examples should be required to train a classifier. | 0 | Unsupervised Models for Named Entity Classification Collins |
Most IE researchers have been creating paraphrase knowledge by hand and specific tasks. | 0 | We proposed an unsupervised method to discover paraphrases from a large untagged corpus. |
They showed the efficacy of graph-based label propagation for projecting part-of-speech information across languages. | 0 | Because all English vertices are going to be labeled, we do not need to disambiguate them by embedding them in trigrams. |
This architecture provides a uniform framework in which it is easy to incorporate not only listed dictionary entries but also morphological derivatives, and models for personal names and foreign names in transliteration. | 0 | It is. based on the traditional character set rather than the simplified character set used in Singapore and Mainland China. |
The approach has been successfully tested on the 8 000-word Verbmobil task. | 0 | The baseline alignment model does not permit that a source word is aligned to two or more target words, e.g. for the translation direction from German toEnglish, the German compound noun 'Zahnarztter min' causes problems, because it must be translated by the two target words dentist's appointment. |
Combining multiple highly-accurate independent parsers yields promising results. | 0 | The entries in this table can be compared with those of Table 3 to see how the performance of the combining techniques degrades in the presence of an inferior parser. |
For the perplexity and translation tasks, we used SRILM to build a 5-gram English language model on 834 million tokens from Europarl v6 (Koehn, 2005) and the 2011 Workshop on Machine Translation News Crawl corpus with duplicate lines removed. | 0 | RandLM 0.2 (Talbot and Osborne, 2007) stores large-scale models in less memory using randomized data structures. |
Explanations for this phenomenon are relative informativeness of lexicalization, insensitivity to morphology and the effect of variable word order and these factors lead to syntactic disambiguation. | 0 | Gabbard and Kulick (2008) show that there is significant attachment ambiguity associated with iDafa, which occurs in 84.3% of the trees in our development set. |
Evaluation was done automatically using the BLEU score and manually on fluency and adequacy. | 0 | We check, how likely only up to k = 20 better scores out of n = 100 would have been generated by two equal systems, using the binomial distribution: If p(0..k; n, p) < 0.05, or p(0..k; n, p) > 0.95 then we have a statistically significant difference between the systems. |
The TRIE model uses less memory than the smallest lossless alternative and is still faster than SRILM. | 0 | In all cases, the key is collapsed to its 64-bit hash. |
The evaluation compares the performance of the system with that of several human judges and inter-human agreement on a single correct way to segment a text. | 0 | In various dialects of Mandarin certain phonetic rules apply at the word. |
The overall performance of their joint framework demonstrated that a probability distribution obtained over mere syntactic contexts using a Treebank grammar and a data-driven lexicon outperformed upper bounds proposed by previous joint disambiguation systems and achieved segmentation and parsing results on a par with state-of-the-art standalone applications results. | 0 | Handling Unknown tokens When handling unknown tokens in a language such as Hebrew various important aspects have to be borne in mind. |
they showed that the average agreement among the human judges is .76, and the average agreement between ST(system) and the humans is .75, or about 99% of the interhuman agreement. | 0 | 4.2 A Sample Segmentation Using Only Dictionary Words Figure 4 shows two possible paths from the lattice of possible analyses of the input sentence B X:Â¥ .:.S:P:l 'How do you say octopus in Japanese?' previously shown in Figure 1. |
they showed that the average agreement among the human judges is .76, and the average agreement between ST(system) and the humans is .75, or about 99% of the interhuman agreement. | 0 | Note that the sets of possible classifiers for a given noun can easily be encoded on that noun by grammatical features, which can be referred to by finite-state grammatical rules. |
In order to create good-sized vectors for similarity calculation, they had to set a high frequency threshold. | 0 | The links can solve the problem. |
The problem of coreference resolution has received considerable attention, including theoretical discourse models and supervised machine learning systems. | 0 | Our coreference resolver performed well in two domains, and experiments showed that each contextual role knowledge source contributed valuable information. |
However, these approaches are ill-equipped to directly represent type-based constraints such as sparsity. | 0 | First, it directly encodes linguistic intuitions about POS tag assignments: the model structure reflects the one-tag-per-word property, and a type- level tag prior captures the skew on tag assignments (e.g., there are fewer unique determiners than unique nouns). |
BABAR's performance in both domains of terrorism and natural disaster, and the contextual-role knowledge in pronouns have shown successful results. | 0 | 6 Conclusions. |
This topic has been getting more attention, driven by the needs of various NLP applications. | 0 | For each set, the phrases with bracketed frequencies are considered not paraphrases in the set. |
The authors use linguistic and annotation insights to develop a manually annotated grammar and evaluate it and finally provide a realistic evaluation in which segmentation is performed in a pipeline jointly with parsing. | 0 | Our results suggest that current parsing models would benefit from better annotation consistency and enriched annotation in certain syntactic configurations. |
Replacing this with a ranked evaluation seems to be more suitable. | 0 | See Figure 3 for a screenshot of the evaluation tool. |
All the texts were annotated by two people. | 0 | For effectively annotating connectives/scopes, we found that existing annotation tools were not well-suited, for two reasons: ⢠Some tools are dedicated to modes of annotation (e.g., tiers), which could only quite un-intuitively be used for connectives and scopes. |
This paper conducted research in the area of automatic paraphrase discovery. | 0 | After tagging a large corpus with an automatic NE tagger, the method tries to find sets of paraphrases automatically without being given a seed phrase or any kind of cue. |
The first method builds on results from (Yarowsky 95) and (Blum and Mitchell 98). | 0 | The method halves the error rate in comparison to a method using the labeled examples alone. |
Here we present two algorithms. | 0 | The weak hypothesis chosen was then restricted to be a predictor in favor of this label. |
The manual evaluation of scoring translation on a graded scale from 1â5 seems to be very hard to perform. | 0 | A few pointed out that adequacy should be broken up into two criteria: (a) are all source words covered? |
Combining multiple highly-accurate independent parsers yields promising results. | 0 | We do not show the numbers for the Bayes models in Table 2 because the parameters involved were established using this set. |
It is probably the first analysis of Arabic parsing of this kind. | 0 | We showed in §2 that lexical ambiguity explains the underperformance of these categories. |
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