On the issue of combining anaphoricity determination and antecedent identification in anaphora resolution

Ryu Iida, Kentaro Inui, Yuji Matsumoto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

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-and-search model proposed by Ng and Cardie [12], but inherits all the advantages of that model. We conducted experiments on resolving noun phrase anaphora in Japanese. The results show that with the classification-and-search based modifications, our proposed model outperforms earlier learning-based approaches.

Original languageEnglish
Title of host publicationProceedings of 2005 IEEE International Conference on Natural Language Processing and Knowledge Engineering, IEEE NLP-KE'05
Pages244-249
Number of pages6
DOIs
Publication statusPublished - 2005 Dec 1
Externally publishedYes
Event2005 IEEE International Conference on Natural Language Processing and Knowledge Engineering, IEEE NLP-KE'05 - Wuhan, China
Duration: 2005 Oct 302005 Nov 1

Publication series

NameProceedings of 2005 IEEE International Conference on Natural Language Processing and Knowledge Engineering, IEEE NLP-KE'05
Volume2005

Other

Other2005 IEEE International Conference on Natural Language Processing and Knowledge Engineering, IEEE NLP-KE'05
CountryChina
CityWuhan
Period05/10/3005/11/1

ASJC Scopus subject areas

  • Engineering(all)

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