Anaphora resolution by antecedent identification followed by anaphoricity determination

Ryu Iida, Kentaro Inui, Yuji Matsumoto

Research output: Contribution to journalReview articlepeer-review

14 Citations (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-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.

Original languageEnglish
Pages (from-to)417-434
Number of pages18
JournalACM Transactions on Asian Language Information Processing
Volume4
Issue number4
DOIs
Publication statusPublished - 2005 Dec 1
Externally publishedYes

Keywords

  • Anaphora resolution
  • Anaphoricity determination
  • Antecedent identification

ASJC Scopus subject areas

  • Computer Science(all)

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