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 NLP-KE’05, October 30, 2005

2 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 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. 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. antecedentanaphor

3 Anaphora resolution can be decomposed into two sub processes 1. Anaphoricity determination is the task of classifying whether a given noun phrase (NP) is anaphoric or non- anaphoric 2. Antecedent identification is the identification of the antecedent of a given anaphoric NP Noun phrase anaphora resolution 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. 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. antecedentanaphor non-anaphor

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 crucially depends on the accuracy of anaphoricity determination

5 Previous work (Cont’d) Previous efforts to tackle anaphoricity determination problem have provided the two findings 1.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) 2.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 combines the strengths of these findings

6 Aim Improving anaphora 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 1.Select the most likely candidate antecedent (CA) of a target NP (TNP) using the tournament model (Iida et al. `03) 2.Classify a TNP paired with CA is classified as anaphoric if CA is identified as the antecedent of TNP; otherwise TNP is judged non-anaphoric

8 2-step process for anaphora resolution 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 …

9 2-step process for anaphora resolution 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 … USAir Group Inc USAir suit USAir Group Inc Federal judge candidate anaphor candidate antecedents … order

10 2-step process for anaphora resolution USAir Group Inc candidate antecedent 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 … Anaphoricity determination model is non-anaphoric USAir score θ ana score θ ana is anaphoric and is the USAir USAir Group Inc antecedent of USAir Group Inc USAir

11 Training phase Anaphoric Non-anaphoric NANP NP5 NP4 NP3 NP2 NP1 Non-anaphoric NP set of candidate antecedents NP3 tournament model candidate antecedent Non-anaphoric instances NP3NANP ANP NP5 NP4 NP3 NP2 NP1 Anaphoric NP set of candidate antecedents Antecedent Anaphoric instances NP4ANP NPi: candidate antecedent

12 Comparison with previous approaches 1. 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 2. 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 SM if the threshold parameters are appropriately tuned disadvantage: not use the contextual information (i.e. whether an appropriate antecedent appears on the context)

13 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 = # of correctly detected anaphoric relations # of anaphoric NPs # of correctly detected anaphoric relations # of NPs classified as anaphoric

14 Experimental setting Conduct 10-fold cross-validation with support vector machines Comparison among three models 1. Search-based model (Ng and Cardie `02) 2. Classification-and-Search model (Ng and Cardie `04) 3. Selection-and-Classification model (Proposed model) using the tournament model (Iida et al. `03)

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

16 Results of noun phrase anaphora resolution Proposed model Search-based model Classification-and- search model Classification-and-search model (CSM) vs. Proposed model the proposed model outperforms the CSM in 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 1.Our model uses non-anaphoric instances together with anaphoric instances to induce anaphoricity classifier 2.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 compatibility e.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  D eveloping a lexical resource which includes a broad range of semantic compatible relations