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1 Exploiting Syntactic Patterns as Clues in Zero- Anaphora Resolution Ryu Iida, Kentaro Inui and Yuji Matsumoto Nara Institute of Science and Technology {ryu-i,inui,matsu}@is.naist.jp June, 20th, 2006
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2 Zero-anaphora resolution Zero-anaphor = a gap with an anaphoric function Zero-anaphora resolution becoming important in many applications In Japanese, even obligatory arguments of a predicate are often omitted when they are inferable from the context 45.5% nominative arguments of verbs are omitted in newspaper articles
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3 Zero-anaphora resolution (cont’d) Three sub-tasks: Zero-pronoun detection: detect a zero-pronoun Antecedent identification : identify the antecedent for a given zero-pronoun Anaphoricity determination : Mary-wa John-ni ( φ -ga ) tabako-o yameru-youni it-ta Mary-NOM John-DAT ( φ -NOM ) smoking-OBJ quit-COMP say-PAST [Mary asked John to quit smoking.] anaphoric zero-pronoun antecedent
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4 Zero-anaphora resolution (cont’d) Three sub-tasks: Zero-pronoun detection: detect a zero-pronoun Antecedent identification : identify antecedent from the set of candidate antecedents for a given zero-pronoun Anaphoricity determination : classify whether a given zero-pronoun is anaphoric or non-anaphoric ( φ -ga ) ie-ni kaeri-tai ( φ -NOM) home-DAT want to go back [(φ=I) want to go home.] non-anaphoric zero-pronoun Mary-wa John-ni ( φ -ga ) tabako-o yameru-youni it-ta Mary-NOM John-DAT ( φ -NOM ) smoking-OBJ quit-COMP say-PAST [Mary asked John to quit smoking.] anaphoric zero-pronoun antecedent
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5 Previous work on anaphora resolution Research trend has been shifting from rule-based approaches (Baldwin, 95; Lappin and Leass, 94; Mitkov, 97, etc.) to empirical, or learning-based, approaches (Soon et al., 2001; Ng 04, Yang et al., 05, etc.) Cost-efficient solution for achieving performance comparable to best performing rule-based systems Learning-based approaches represent a problem, anaphoricity determination and antecedent identification, as a set of feature vectors and apply machine learning algorithms to them
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6 Useful clues for both anaphoricity determination and antecedent identification Syntactic pattern features Mary-wa Mary-TOP predicate yameru-youni quit-CONP zero-pronoun φ-ga φ-NOM predicate it-ta say-PAST Antecedent John-ni John-DAT tabako-o smoking-OBJ
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7 Useful clues for both anaphoricity determination and antecedent identification Questions How to encode syntactic patterns as features How to avoid data sparseness problem Syntactic pattern features Mary-wa Mary-TOP predicate yameru-youni quit-CONP zero-pronoun φ-ga φ-NOM predicate it-ta say-PAST Antecedent John-ni John-DAT tabako-o smoking-OBJ
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8 Talk outline 1. Zero-anaphora resolution: Background 2. Selection-then-classification model (Iida et al., 05) 3. Proposed model Represents syntactic patterns based on dependency trees Uses a tree mining technique to seek useful sub-trees to solve data sparseness problem Incorporates syntactic pattern features in the selection-then-classification model 4. Experiments on Japanese zero-anaphora 5. Conclusion and future work
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9 A federal judge in Pittsburgh issued a temporary restraining order preventing Trans World Airlines from buying additional shares of USAir Group Inc. The order, requested in a suit filed by USAir, … candidate anaphor tournament model USAir suit USAir Group Inc order federal judge candidate anaphor candidate antecedents … Selection-then-Classification Model (SCM) (Iida et al., 05)
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10 tournament model USAir suit USAir Group Inc order federal judge candidate anaphor candidate antecedents … USAir Group Inc USAir suit USAir Group Inc Federal judge candidate anaphor candidate antecedents … order Selection-then-Classification Model (SCM) (Iida et al., 05) (Iida et al. 03)
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11 USAir Group Inc most likely candidate antecedent tournament model USAir suit USAir Group Inc order federal judge candidate anaphor candidate antecedents … Selection-then-Classification Model (SCM) (Iida et al., 05)
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12 USAir Group Inc most likely candidate antecedent tournament model USAir suit USAir Group Inc order federal judge candidate anaphor candidate antecedents … is non-anaphoric USAir score θ ana score ≧ θ ana is anaphoric and is the USAir USAir Group Inc antecedent of Anaphoricity determination model USAir Group Inc USAir Selection-then-Classification Model (SCM) (Iida et al., 05)
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13 USAir Group Inc most likely candidate antecedent tournament model USAir suit USAir Group Inc order federal judge candidate anaphor candidate antecedents … is non-anaphoric USAir score θ ana score ≧ θ ana is anaphoric and is the USAir USAir Group Inc antecedent of Anaphoricity determination model USAir Group Inc USAir Selection-then-Classification Model (SCM) (Iida et al., 05)
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14 Anaphoric Non-anaphoric NANP NP5 NP4 NP3 NP2 NP1 non-anaphoric noun phrase set of candidate antecedents NP3 tournament model candidate antecedent Non-anaphoric instances NP3NANP ANP NP5 NP4 NP3 NP2 NP1 anaphoric noun phrase set of candidate antecedents Antecedent Anaphoric instances NP4ANP NPi: candidate antecedent Training the anaphoricity determination model
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15 Talk outline 1. Zero-anaphora resolution: Background 2. Selection-then-classification model (Iida et al., 05) 3. Proposed model Represents syntactic patterns based on dependency trees Uses a tree mining technique to seek useful sub-trees to solve data sparseness problem Incorporates syntactic pattern features in the selection-then-classification model 4. Experiments on Japanese zero-anaphora 5. Conclusion and future work
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16 USAir Group Inc most likely candidate antecedent tournament model USAir suit USAir Group Inc order federal judge candidate antecedents … is non-anaphoric USAir score θ ana score ≧ θ ana is anaphoric and is the USAir USAir Group Inc antecedent of Anaphoricity determination model USAir Group Inc USAir New model candidate anaphor
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17 Use of syntactic pattern features Encoding parse tree features Learning useful sub-trees
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18 Encoding parse tree features Mary-wa Mary-TOP predicate yameru-youni quit-CONP zero-pronoun φ-ga φ-NOM predicate it-ta say-PAST Antecedent John-ni John-DAT tabako-o smoking-OBJ
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19 Encoding parse tree features predicate yameru-youni quit-CONP zero-pronoun φ-ga φ-NOM predicate it-ta say-PAST Antecedent John-ni John-DAT Mary-wa Mary-TOP tabako-o smoking-OBJ
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20 Encoding parse tree features Antecedent predicate zero-pronoun predicate predicate yameru-youni quit-CONP zero-pronoun φ-ga φ-NOM predicate it-ta say-PAST Antecedent John-ni John-DAT
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21 Encoding parse tree features Antecedent predicate zero-pronoun predicate youni CONJ ni DAT ga CONJ ta PAST predicate yameru-youni quit-CONP zero-pronoun φ-ga φ-NOM predicate it-ta say-PAST Antecedent John-ni John-DAT
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22 Encoding parse trees LeftCand predicate RightCand (TI)(TI) LeftCand predicate zero- pronoun predicate (TL)(TL) RightCand (TR)(TR) predicate zero- pronoun predicate LeftCand Mary-wa Mary-TOP predicate yameru-youni quit-CONP zero-pronoun φ-ga φ-NOM predicate it-ta say-PAST RightCand John-ni John-DAT tabako-o smoking-OBJ
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23 Encoding parse trees Antecedent identification root Three sub-trees
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24 Encoding parse trees Antecedent identification root Three sub-trees 1 2 n … … Lexical, Grammatical, Semantic, Positional and Heuristic binary features
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25 Encoding parse trees Antecedent identification root 1 2 n … … Three sub-trees Lexical, Grammatical, Semantic, Positional and Heuristic binary features Left or right label
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26 Learning useful sub-trees Kernel methods: Tree kernel (Collins and Duffy, 01) Hierarchical DAG kernel (Suzuki et al., 03) Convolution tree kernel (Moschitti, 04) Boosting-based algorithm: BACT (Kudo and Matsumoto, 04) system learns a list of weighted decision stumps with the Boosting algorithm
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27 positive Boosting-based algorithm: BACT Learns a list of weighted decision stumps with Boosting Classifies a given input tree by weighted voting Learning useful sub-trees positive Labels Training instances …. 0.4 weight Label positive sub-tree decision stumps learn Score: +0.34 positive apply
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28 Overall process Input (a zero-pronoun φ in the sentence S ) Intra-sentential model Inter-sentential model score intra < θ intra score intra ≧ θ intra Output the most-likely candidate antecedent appearing in S score inter ≧ θ inter Output the most-likely candidate appearing outside of S score inter < θ inter Return ‘‘non-anaphoric’’ syntactic patterns
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29 Table of contents 1. Zero-anaphora resolution 2. Selection-then-classification model (Iida et al., 05) 3. Proposed model Parse encoding Tree mining 4. Experiments 5. Conclusion and future work
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30 Japanese newspaper article corpus comprising zero- anaphoric relations: 197 texts (1,803 sentences) 995 intra-sentential anaphoric zero-pronouns 754 inter-sentential anaphoric zero-pronouns 603 non-anaphoric zero-pronouns Recall = Precision = Experiments # of correctly resolved zero-anaphoric relations # of anaphoric zero-pronouns # of anaphoric zero-pronouns the model detected # of correctly resolved zero-anaphoric relations
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31 Experimental settings Conducting five-fold cross validation Comparison among four models BM : Ng and Cardie (02)’s model: Identify an antecedent with candidate-wise classification Determine the anaphoricity of a given anaphor as a by- product of the search for its antecedent BM_STR : BM +syntactic pattern features SCM : Selection-then-classification model (Iida et al., 05) SCM_STR : SCM + syntactic pattern features
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32 Results of intra-sentential ZAR Antecedent identification (accuracy) The performance of antecedent identification improved by using syntactic pattern features BM (Ng02)BM_STRSCM (Iida05)SCM_STR 48.0% (478/995) 63.5% (632/995) 65.1% (648/995) 70.5% (701/995)
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33 antecedent identification + anaphoricity determination Results of intra-sentential ZAR
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34 Impact on overall ZAR Evaluate the overall performance for both intra- sentential and inter-sentential ZAR Baseline model: SCM resolves intra-sentential and inter-sentential zero-anaphora simultaneously with no syntactic pattern features.
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35 Results of overall ZAR
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36 AUC curve AUC (Area Under the recall-precision Curve) plotted by altering θ intra Not peaky optimizing parameter θ intra is not difficult
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37 Conclusion We have addressed the issue of how to use syntactic patterns for zero-anaphora resolution. How to encode syntactic pattern features How to seek useful sub-trees Incorporating syntactic pattern features into our selection-then- classification model improves the accuracy for intra-sentential zero-anaphora, which consequently improves the overall performance of zero-anaphora resolution
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38 Future work How to find zero-pronouns? Designing a broader framework to interact with analysis of predicate argument structure How to find a globally optimal solution to the set of zero-anaphora resolution problems in a given discourse? Exploring methods as discussed by McCallum and Wellner (03)
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