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They used graph-based label propagation for cross-lingual knowledge transfer and used the projected labels as features in an unsupervised model.
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This is similar to stacking the different feature instantiations into long (sparse) vectors and computing the cosine similarity between them.
It also incorporates the Good-Turing methodin estimating the likelihoods of previously unseen constructions, including morphological derivatives and personal names.
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Some approaches depend upon some form of con­ straint satisfaction based on syntactic or semantic features (e.g., Yeh and Lee [1991], which uses a unification-based approach).
Other kinds of productive word classes, such as company names, abbreviations,and place names can easily be handled given appropriate models.
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computing the precision of the other's judgments relative to this standard.
Most IE researchers have been creating paraphrase knowledge by hand and specific tasks.
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We would like to thank Prof. Ralph Grish- man, Mr. Takaaki Hasegawa and Mr. Yusuke Shinyama for useful comments, discussion and evaluation.
The AdaBoost algorithm was developed for supervised learning.
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, for A. T.&T. nonalpha.. .
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.
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An extension of the TAG system was introduced by Joshi et al. (1975) and later redefined by Joshi (1987) in which the adjunction operation is defined on sets of elementary trees rather than single trees.
They proposed an unsupervised method to discover paraphrases from a large untagged corpus.
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We checked whether the discovered links are listed in WordNet.
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.
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The parser builds dependency graphs by traversing the input from left to right, using a stack to store tokens that are not yet complete with respect to their dependents.
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.
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We refer to a segment and its assigned PoS tag as a lexeme, and so analyses are in fact sequences of lexemes.
In this work, they take a more direct approach and treat a word type and its allowed POS tags as a primary element of the model.
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Second, the reduced number of hidden variables and parameters dramatically speeds up learning and inference.
The departure from the traditional token-based tagging approach allow them to explicitly capture type-level distributional properties of valid POS tag assignments as part of the model.
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t(i).
The approach assumes that the word reordering is restricted to a few positions in the source sentence.
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A modified language model probability pÆ(eje0; e00) is defined as follows: pÆ(eje0; e00) = 1:0 if Æ = 0 p(eje0; e00) if Æ = 1 : We associate a distribution p(Æ) with the two cases Æ = 0 and Æ = 1 and set p(Æ = 1) = 0:7.
The bias of automatic methods in favour of statistical systems seems to be less pronounced on out-of-domain test data.
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Let say, if we find one system doing better on 20 of the blocks, and worse on 80 of the blocks, is it significantly worse?
This assumption, however, is not inherent to type-based tagging models.
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The P (T |ψ) distribution, in English for instance, should have very low mass for the DT (determiner) tag, since determiners are a very small portion of the vocabulary.
This paper conducted research in the area of automatic paraphrase discovery.
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There has been a lot of research on such lexical relations, along with the creation of resources such as WordNet.
The resulting model is compact, efficiently learnable and linguistically expressive.
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41.
This paper presents a maximum entropy-based named entity recognizer (NER).
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Dictionaries: Due to the limited amount of training material, name dictionaries have been found to be useful in the named entity task.
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.
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Roughly speaking, we say that a tee set contains trees with dependent paths if there are two paths p., = vim., and g., = in each 7 E r such that v., is some, possibly empty, shared initial subpath; v., and wi are not bounded in length; and there is some "dependence" (such as equal length) between the set of all v., and w., for each 7 Er.
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.
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But for eign learners are often surprised by the verbless predications that are frequently used in Arabic.
Due to many similarly performing systems, they are not able to draw strong conclusions on the question of correlation of manual and automatic evaluation metrics.
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We dropped, however, one of the languages, Finnish, partly to keep the number of tracks manageable, partly because we assumed that it would be hard to find enough Finnish speakers for the manual evaluation.
The second algorithm builds on a boosting algorithm called AdaBoost.
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The contextual rules are restricted and may not be applicable to every example, but the spelling rules are generally applicable and should have good coverage.
Most IE researchers have been creating paraphrase knowledge by hand and specific tasks.
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[Hasegawa et al. 04] reported only on relation discovery, but one could easily acquire para phrases from the results.
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.
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The second weakness is purely conceptual, and probably does not affect the per­ formance of the model.
It is probably the first analysis of Arabic parsing of this kind.
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35 76.
Using less training data than other systems, their NER can perform as well as other state-of-the-art NERs.
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(1999) did not report using any dictionaries, but mentioned in a footnote that they have added list membership features, which have helped marginally in certain domains.
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.
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This model is easily incorporated into the segmenter by building a WFST restrict­ ing the names to the four licit types, with costs on the arcs for any particular name summing to an estimate of the cost of that name.
This paper discusses the Potsdam Commentary Corpus, a corpus of german assembeled by potsdam university.
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Nonetheless there is no alternative to Reiche’s plan.
In order to handle the necessary word reordering as an optimization problem within the dynamic programming approach, they describe a solution to the traveling salesman problem (TSP) which is based on dynamic programming.
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We apply a beam search concept as in speech recognition.
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.
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In this section, we describe the seven general knowledge sources and explain how the DempsterShafer model makes resolutions.
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.
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Our analysis identifies three key factors driving our performance gain: 1) selecting a model structure which directly encodes tag sparsity, 2) a type-level prior on tag assignments, and 3) a straightforward na¨ıveBayes approach to incorporate features.
Their results show that their high performance NER use less training data than other systems.
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As each global feature group is added to the list of features, we see improvements to both MUC6 and MUC6 MUC7 Baseline 90.75% 85.22% + ICOC 91.50% 86.24% + CSPP 92.89% 86.96% + ACRO 93.04% 86.99% + SOIC 93.25% 87.22% + UNIQ 93.27% 87.24% Table 3: F-measure after successive addition of each global feature group Table 5: Comparison of results for MUC6 Systems MUC6 MUC7 No.
This assumption, however, is not inherent to type-based tagging models.
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we perform five runs with different random initialization of sampling state.
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.
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For example, both a chair and a truck would be labeled as artifacts, but this does not at all suggest that they are coreferent.
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.
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orthographic words are thus only a starting point for further analysis and can only be regarded as a useful hint at the desired division of the sentence into words.
It outperformed strong unsupervised baselines as well as approaches that relied on direct projections, and bridged the gap between purely supervised and unsupervised POS tagging models.
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We tried two versions of our graph-based approach: feature after the first stage of label propagation (Eq.
They proposed an unsupervised method to discover paraphrases from a large untagged corpus.
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Step 2.
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.
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Phrase-level granularity distinguishes our work from previous work by Matsoukas et al (2009), who weight sentences according to sub-corpus and genre membership.
Their method did not assume any knowledge about the target language, making it applicable to a wide array of resource-poor languages.
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To bridge this gap, we consider a practically motivated scenario, in which we want to leverage existing resources from a resource-rich language (like English) when building tools for resource-poor foreign languages.1 We assume that absolutely no labeled training data is available for the foreign language of interest, but that we have access to parallel data with a resource-rich language.
the "Potsdam Commentary Corpus" or PCC consists of 170 commentaries from Ma¨rkische Allgemeine Zeitung, a German regional daily.
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3.2 Stochastic rhetorical analysis.
This paper talks about KenLM: Faster and Smaller Language Model Queries.
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The results in Table 1 show PROBING is 81% faster than TRIE, which is in turn 31% faster than the fastest baseline.
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.
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The pre terminal morphological analyses are mapped to the shortened “Bies” tags provided with the tree- bank.
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.
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For each pair of judges, consider one judge as the standard,.
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.
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The aforementioned surface form bcl, for example, may also stand for the lexical item “onion”, a Noun.
The model incorporates various recent techniques for incorporating and manipulating linguistic knowledge using finite-state transducers.
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We also thank ChaoHuang Chang, reviewers for the 1994 ACL conference, and four anonymous reviewers for Computational Linguistics for useful comments.
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.
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As we have seen, the lexicon of basic words and stems is represented as a WFST; most arcs in this WFST represent mappings between hanzi and pronunciations, and are costless.
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.
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And if one is interested in TIS, one would probably consider the single orthographic word ACL to consist of three phonological words-lei s'i d/-corresponding to the pronunciation of each of the letters in the acronym.
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.
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Reading the following record’s offset indicates where the block ends.
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.
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The development of the very first Hebrew Treebank (Sima’an et al., 2001) called for the exploration of general statistical parsing methods, but the application was at first limited.
This paper talks about Pseudo-Projective Dependency Parsing.
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In the present study, we limit ourselves to an algorithmic approach, using a deterministic breadthfirst search.
Nevertheless, only a part of this corpus (10 texts), which the authors name "core corpus", is annotated with all this information.
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This concerns on the one hand the basic question of retrieval, i.e. searching for information across the annotation layers (see 3.1).
It also incorporates the Good-Turing methodin estimating the likelihoods of previously unseen constructions, including morphological derivatives and personal names.
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The most popular approach to dealing with seg­ mentation ambiguities is the maximum matching method, possibly augmented with further heuristics.
The AdaBoost algorithm was developed for supervised learning.
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A spelling rule might be a simple look-up for the string (e.g., a rule that Honduras is a location) or a rule that looks at words within a string (e.g., a rule that any string containing Mr. is a person).
This topic has been getting more attention, driven by the needs of various NLP applications.
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Figure 2 shows examples of extracted NE pair instances and their contexts.
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.
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Specifically, for both settings we report results on the median run for each setting.
BABAR's performance in both domains of terrorism and natural disaster, and the contextual-role knowledge in pronouns have shown successful results.
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8 Volume 1, English language, 19961997, Format version 1, correction level 0 An ap ho r T e r r o r i s m R e c Pr F D i s a s t e r s R e c Pr F De f. NP s Pro no uns .43 .79 .55 .50 .72 .59 .42 .91 .58 .42 .82 .56 Tot al .46 .76 .57 .42 .87 .57 Table 2: General Knowledge Sources Table 4: Individual Performance of KSs for Terrorism Table 3: General + Contextual Role Knowledge Sources larger MUC4 and Reuters corpora.9 4.2 Experiments.
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.
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English parsing evaluations usually report results on sentences up to length 40.
The manual evaluation of scoring translation on a graded scale from 1–5 seemed to be very hard to perform.
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The text type are editorials instead of speech transcripts.
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.
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For each terminal, the Leaf Ancestor metric extracts the shortest path to the root.
However, using the top-level semantic classes of WordNet proved to be problematic as the class distinctions are too coarse.
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In this situation, BABAR takes the conservative approach and declines to make a resolution.
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.
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2.
It is probably the first analysis of Arabic parsing of this kind.
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But Arabic contains a variety of linguistic phenomena unseen in English.
Other kinds of productive word classes, such as company names, abbreviations,and place names can easily be handled given appropriate models.
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The transitive closure of the dictionary in (a) is composed with Id(input) (b) to form the WFST (c).
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.
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Purely statistical approaches have not been very popular, and so far as we are aware earlier work by Sproat and Shih (1990) is the only published instance of such an approach.
They believe that global context is useful in most languages, as it is a natural tendency for authors to use abbreviations on entities already mentioned previously.
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Hence, there is a total of 29 classes (7 name classes 4 sub-classes 1 not-a-name class).
In order to handle the necessary word reordering as an optimization problem within the dynamic programming approach, they describe a solution to the traveling salesman problem (TSP) which is based on dynamic programming.
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2) An improved language model, which takes into account syntactic structure, e.g. to ensure that a proper English verbgroup is generated.
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.
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We have presented a new method for non-projective dependency parsing, based on a combination of data-driven projective dependency parsing and graph transformation techniques.
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.
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As will be obvious later, their derivation tree sets will be local sets as are those of CFG's.
This paper offers a broad insight into of Arabic constituency parsing by analyzing the interplay of linguistic phenomena, annotation choices, and model design.
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Since our objective is to compare distributions of bracketing discrepancies, we do not use heuristics to prune the set of nuclei.
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.
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We can better predict the probability of an unseen hanzi occurring in a name by computing a within-class Good-Turing estimate for each radical class.
The manual evaluation of scoring translation on a graded scale from 1–5 seemed to be very hard to perform.
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If two systems’ scores are close, this may simply be a random effect in the test data.
The contextual rules are restricted and may not be applicable to every example, but the spelling rules are generally applicable and should have good coverage.
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In principle a feature could be an arbitrary predicate of the (spelling, context) pair; for reasons that will become clear, features are limited to querying either the spelling or context alone.
Replacing this with a ranked evaluation seems to be more suitable.
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Many human evaluation metrics have been proposed.
Most IE researchers have been creating paraphrase knowledge by hand and specific tasks.
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We are focusing on phrases which have two Named Entities (NEs), as those types of phrases are very important for IE applications.
The bias of automatic methods in favour of statistical systems seems to be less pronounced on out-of-domain test data.
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The manual evaluation of scoring translation on a graded scale from 1–5 seems to be very hard to perform.
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.
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As a result, CFG's can not provide the structural descriptions in which there are nested dependencies between symbols labelling a path.
The experimental tests are carried out on the Verbmobil task which is a limited-domain spoken-language task.
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What is important and is not expressed by the notation is the so-called coverage constraint: each source position j should be 'hit' exactly once by the path of the inverted alignment bI 1 = b1:::bi:::bI . Using the inverted alignments in the maximum approximation, we obtain as search criterion: max I (p(JjI) max eI 1 ( I Yi=1 p(eijei􀀀1 i􀀀2) max bI 1 I Yi=1 [p(bijbi􀀀1; I; J) p(fbi jei)])) = = max I (p(JjI) max eI 1;bI 1 ( I Yi=1 p(eijei􀀀1 i􀀀2) p(bijbi􀀀1; I; J) p(fbi jei)])); where the two products over i have been merged into a single product over i. p(eijei􀀀1 i􀀀2) is the trigram language model probability.
They focused on phrases which two Named Entities, and proceed in two stages.
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Using structural information As was explained in the results section, we extracted examples like “Smith estimates Lotus”, from a sentence like “Mr.
This paper discusses the Potsdam Commentary Corpus, a corpus of german assembeled by potsdam university.
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Section 4 draws some conclusions from the present state of the effort.
The problem of coreference resolution has received considerable attention, including theoretical discourse models and supervised machine learning systems.
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The role that each noun phrase plays in the kidnapping event is key to distinguishing these cases.
An extended lexicon model is defined, and its likelihood is compared to a baseline lexicon model, which takes only single-word dependencies into account.
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An inverted alignment is defined as follows: inverted alignment: i ! j = bi: Target positions i are mapped to source positions bi.
Their results show that their high performance NER use less training data than other systems.
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Suffixes and Prefixes: This group contains only two features: Corporate-Suffix and Person-Prefix.
However, these approaches are ill-equipped to directly represent type-based constraints such as sparsity.
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Our second point of comparison is with Grac¸a et al.
It also incorporates the Good-Turing methodin estimating the likelihoods of previously unseen constructions, including morphological derivatives and personal names.
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For example Chen and Liu (1992) report precision and recall rates of over 99%, but this counts only the words that occur in the test corpus that also occur in their dictionary.
It also incorporates the Good-Turing methodin estimating the likelihoods of previously unseen constructions, including morphological derivatives and personal names.
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One class comprises words derived by productive morphologi­ cal processes, such as plural noun formation using the suffix ir, menD.
The AdaBoost algorithm was developed for supervised learning.
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An edge indicates that the two features must have the same label.
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.
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It is unique, agrees with the maximum-likelihood distribution, and has the exponential form (Della Pietra et al., 1997): where refers to the outcome, the history (or context), and is a normalization function.
The bias of automatic methods in favour of statistical systems seems to be less pronounced on out-of-domain test data.
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However, it is also mostly political content (even if not focused on the internal workings of the European Union) and opinion.
Here we show how non-projective dependency parsing can be achieved by combining a data driven projective parser with special graph transformation techniques.
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The baseline simply retains the original labels for all arcs, regardless of whether they have been lifted or not, and the number of distinct labels is therefore simply the number n of distinct dependency types.2 In the first encoding scheme, called Head, we use a new label d↑h for each lifted arc, where d is the dependency relation between the syntactic head and the dependent in the non-projective representation, and h is the dependency relation that the syntactic head has to its own head in the underlying structure.
The bias of automatic methods in favour of statistical systems seemed to be less pronounced on out-of-domain test data.
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One may argue with these efforts on normalization, and ultimately their value should be assessed by assessing their impact on inter-annotator agreement.
Each out-of-domain phrase pair was characterized by a set of simple features intended to reflect how useful it would be.
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We train linear mixture models for conditional phrase pair probabilities over IN and OUT so as to maximize the likelihood of an empirical joint phrase-pair distribution extracted from a development set.
Their results show that their high performance NER use less training data than other systems.
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Borth- wick (1999) successfully made use of other hand- coded systems as input for his MENE system, and achieved excellent results.
Koehn and Monz carried out an extensive manual and automatic evaluation of machine translation performance on European language pairs.
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In words, the judgements are normalized, so that the average normalized judgement per judge is 3.
This assumption, however, is not inherent to type-based tagging models.
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This paper proposes a simple and effective tagging method that directly models tag sparsity and other distributional properties of valid POS tag assignments.
There is no global pruning.
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For our demonstration system, we typically use the pruning threshold t0 = 5:0 to speed up the search by a factor 5 while allowing for a small degradation in translation accuracy.
In order to create good-sized vectors for similarity calculation, they had to set a high frequency threshold.
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Notice that the CC-domain is a special case.
It also incorporates the Good-Turing methodin estimating the likelihoods of previously unseen constructions, including morphological derivatives and personal names.
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In these examples, the names identified by the two systems (if any) are underlined; the sentence with the correct segmentation is boxed.19 The differences in performance between the two systems relate directly to three issues, which can be seen as differences in the tuning of the models, rather than repre­ senting differences in the capabilities of the model per se.
Combining multiple highly-accurate independent parsers yields promising results.
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If we were working with more than three parsers we could investigate minority constituents, those constituents that are suggested by at least one parser, but which the majority of the parsers do not suggest.
They have made use of local and global features to deal with the instances of same token in a document.
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In fact, training on the official training data is not suitable as the articles in this data set are entirely about aviation disasters, and the test data is about air vehicle launching.
Using less training data than other systems, their NER can perform as well as other state-of-the-art NERs.
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Mikheev et al.
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.
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Our model outperforms theirs on four out of five languages on the best hyperparameter setting and three out of five on the median setting, yielding an average absolute difference across languages of 12.9% and 3.9% for best and median settings respectively compared to their best EM or LBFGS performance.