Anaphora resolution by antecedent identification followed by anaphoricity determination

We propose a machine learning-based approach to noun-phrase anaphora resolution that combines the advantages of previous learning-based models while overcoming their drawbacks. Our anaphora resolution process reverses the order of the steps in the classification-then-search model proposed by Ng and Cardie [2002b], inheriting all the advantages of that model. We conducted experiments on resolving noun-phrase anaphora in Japanese. The results show that with the selection-then-classification-based modifications, our proposed model outperforms earlier learning-based approaches.

[1]  Yuji Matsumoto,et al.  Japanese Morphological Analysis System ChaSen version 2.0 Manual , 1999 .

[2]  Jian Su,et al.  Coreference Resolution Using Competition Learning Approach , 2003, ACL.

[3]  Manabu Okumura,et al.  Zero Pronoun Resolution in Japanese Discourse Based on Centering Theory , 1996, COLING.

[4]  R. Iida,et al.  Incorporating Contextual Cues in Trainable Models for Coreference Resolution , 2003 .

[5]  N. H. Beebe.  A Complete Bibliography of ACM Transactions on Asian Language Information Processing , 2007 .

[6]  Shalom Lappin,et al.  An Algorithm for Pronominal Anaphora Resolution , 1994, CL.

[7]  Claire Cardie,et al.  Combining Sample Selection and Error-Driven Pruning for Machine Learning of Coreference Rules , 2002, EMNLP.

[8]  Aravind Joshi,et al.  Cogniac: a discourse processing engine , 1995 .

[9]  Carl Pollard,et al.  A Centering Approach to Pronouns , 1987, ACL.

[10]  Hwee Tou Ng,et al.  A Machine Learning Approach to Coreference Resolution of Noun Phrases , 2001, CL.

[11]  Michael Strube,et al.  A Machine Learning Approach to Pronoun Resolution in Spoken Dialogue , 2003, ACL.

[12]  Claire Cardie,et al.  Identifying Anaphoric and Non-Anaphoric Noun Phrases to Improve Coreference Resolution , 2002, COLING.

[13]  Renata Vieira,et al.  Discourse-New Detectors for Definite Description Resolution: A Survey and a Preliminary Proposal , 2004 .

[14]  Ellen Riloff,et al.  Corpus-Based Identification of Non-Anaphoric Noun Phrases , 1999, ACL.

[15]  John Hale,et al.  A Statistical Approach to Anaphora Resolution , 1998, VLC@COLING/ACL.

[16]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[17]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[18]  Yuji Matsumoto,et al.  Japanese Dependency Analysis using Cascaded Chunking , 2002, CoNLL.

[19]  Vincent Ng,et al.  Learning Noun Phrase Anaphoricity to Improve Conference Resolution: Issues in Representation and Optimization , 2004, ACL.

[20]  James V. Candy,et al.  Adaptive and Learning Systems for Signal Processing, Communications, and Control , 2006 .

[21]  Olga Uryupina,et al.  High-precision Identification of Discourse New and Unique Noun Phrases , 2003, ACL.

[22]  Ruslan Mitkov.  Factors in anaphora resolution: they are not the only things that matter. A case study based on two different approaches , 1997 .

[23]  Andrew McCallum,et al.  Object Consolodation by Graph Partitioning with a Conditionally›Trained Distance Metric , 2003 .

[24]  Scott Weinstein,et al.  Centering: A Framework for Modeling the Local Coherence of Discourse , 1995, CL.

[25]  Satoshi Shirai,et al.  Anaphora Resolution of Japanese Zero Pronouns with Deictic Reference , 1996, COLING.

[26]  Claire Gardent,et al.  Improving Machine Learning Approaches to Coreference Resolution , 2002, ACL.

[27]  Wendy G. Lehnert,et al.  Using Decision Trees for Coreference Resolution , 1995, IJCAI.