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On the Issue of Combining Anaphoricity Determination and Antecedent Identification in Anaphora Resolution

On the Issue of Combining Anaphoricity Determination and Antecedent Identification in Anaphora Resolution. Ryu Iida, Kentaro Inui, Yuji Matsumoto Nara Institute of Science and Technology {ryu-i,inui,matsu}@is.naist.jp NLP-KE’05, October 30, 2005.

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On the Issue of Combining Anaphoricity Determination and Antecedent Identification in Anaphora Resolution

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  1. On the Issue of Combining Anaphoricity Determination and Antecedent Identification in Anaphora Resolution Ryu Iida, Kentaro Inui, Yuji Matsumoto Nara Institute of Science and Technology {ryu-i,inui,matsu}@is.naist.jp NLP-KE’05, October 30, 2005

  2. 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, dealt another blow to TWA's bid to buy the company for $52 a share. antecedent anaphor Noun phrase anaphora resolution • Anaphora resolution is the process of determining whether two expressions in natural language refer to the same real world entity • Important process for various NLP applications: machine translation, information extraction, question answering

  3. 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, dealt another blow to TWA's bid to buy the company for $52 a share. antecedent anaphor non-anaphor Noun phrase anaphora resolution • Anaphora resolution can be decomposed into two sub processes • Anaphoricity determination is the task of classifying whether a given noun phrase (NP) is anaphoric or non-anaphoric • Antecedent identification is the identification of the antecedent of a given anaphoric NP

  4. Previous work • Early corpus-based work on anaphora resolution does not address anaphoricity determination (Hobbs `78, Lappin and Leass `94) • Assuming that the anaphora resolution system knows a priori all the anaphoric noun phrases • This problem has been paid attention by an increasing number of researchers (Bean and Riloff `99, Ng and Cardie `02, Uryupina `03, Ng `04) • Determining anaphoricity is not a trivial problem • Overall performance of anaphora resolution cruciallydepends on the accuracy of anaphoricity determination

  5. Previous work (Cont’d) • Previous efforts to tackle anaphoricity determination problem have provided the two findings • One useful cue for determining anaphoricity of a given NP can be obtained by searching for an antecedent(Soon et al. 01, Ng and Cardie 02a) • Anaphoricity determination can be effectively carried out by a binary classifier that learns instances of non-anaphoric NPs (Ng and Cardie 02b, Ng 04) • None of the previous models effectively combinesthe strengths of these findings

  6. Aim • Improvinganaphora resolution performance: • Using better anaphoricity determination • Combining sources of evidence from previous models

  7. Proposal • Introducing a 2-step process for combining antecedent information and non-anaphoric information • We call this model the selection-and-classification model • Select the most likely candidate antecedent (CA) of a target NP (TNP) using the tournament model (Iida et al. `03) • Classify a TNP paired with CA is classified asanaphoricif CA is identified as the antecedent of TNP; otherwise TNP is judgednon-anaphoric

  8. 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 federal judge tournament model candidate antecedents order … USAir Group Inc suit candidate anaphor USAir 2-step process for anaphora resolution

  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 federal judge tournament model candidate antecedents order … USAir Group Inc USAir Group Inc suit candidate anaphor USAir … USAir Group Inc Federal judge order USAir suit candidate anaphor candidate antecedents 2-step process for anaphora resolution

  10. 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 federal judge tournament model candidate antecedents order … USAir Group Inc USAir USAir Group Inc Anaphoricitydetermination model suit score θ ana candidate anaphor USAir scoreθ ana is anaphoric and USAir is non-anaphoric USAir is the antecedent of USAir Group Inc USAir 2-step process for anaphora resolution USAir Group Inc candidate antecedent

  11. Anaphoricinstances NP4 ANP tournament model NP3 NANP Non-anaphoricinstances candidate antecedent NP3 Training phase • Anaphoric • Non-anaphoric NP1 set of candidate antecedents NPi: candidate antecedent NP2 NP3 Antecedent NP4 NP5 Anaphoric NP ANP NP1 set of candidate antecedents NP2 NP3 NP4 NP5 Non-anaphoric NP NANP

  12. Comparison with previous approaches • Search-based approach (SM)(Soon et al. `01, Ng and Cardie `02) • Recasting anaphora resolution as binary classification problems • Comparable to the state-of-the-art rule-based system • disadvantage: not use non-anaphoric instances in training • Classification-and-search approach (CSM) (Ng and Cardie `02, Ng `04) • Introducing anaphoricity determination as a classification task • The performance of the CSM is better than the SMif the threshold parameters are appropriately tuned • disadvantage:not use the contextual information(i.e. whether an appropriate antecedent appears on the context)

  13. # of correctly detected anaphoric relations # of correctly detected anaphoric relations # of NPs classified as anaphoric # of anaphoric NPs Experiments • Noun phrase anaphora resolution in Japanese • Japanese newspaper article corpus tagged NP-anaphoric relations • 90 text, 1,104 sentences • Noun phrases : 876 anaphors and 6,292 non-anaphors Recall = Precision =

  14. Experimental setting • Conduct 10-fold cross-validation with support vector machines • Comparison among three models • Search-based model (Ng and Cardie `02) • Classification-and-Search model (Ng and Cardie `04) • Selection-and-Classification model (Proposed model) using the tournament model (Iida et al. `03)

  15. Results of noun phrase anaphora resolution Proposed model Classification-and-search model Search-based model Search-basedmodel (SM) vs. Classification-and-search model (CSM)the performance of CSM is significantlybetter than the SM

  16. Results of noun phrase anaphora resolution Proposed model Classification-and-search model Search-based model Classification-and-search model (CSM) vs.Proposed model the proposed model outperforms the CSMin the higher-recall portion

  17. Conclusion • Our selection-and-classification approach to anaphora resolution improves on the performance of previous learning-based models by combining their advantages • Our model uses non-anaphoric instances together with anaphoric instances to induce anaphoricity classifier • Our model determines the anaphoricity of a given NP by taking antecedent information into account

  18. Future work • The majority of errors are caused by the difficulty of judging the semantic compatibilitye.g.) the system outputs that “ani (elder brother)” is anaphoric with “kanojo (she)” • The lexical resource we employed in the experiments did not contain gender information Developing a lexical resource which includes a broad range of semantic compatible relations

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