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Chinese Zero Pronoun Resolution: A Collaborative Filtering-based Approach

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Published:05 June 2019Publication History
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Abstract

Semantic information that has been proven to be necessary to the resolution of common noun phrases is typically ignored by most existing Chinese zero pronoun resolvers. This is because that zero pronouns convey no descriptive information, which makes it almost impossible to calculate semantic similarities between the zero pronoun and its candidate antecedents. Moreover, most of traditional approaches are based on the single-candidate model, which considers the candidate antecedents of a zero pronoun in isolation and thus overlooks their reciprocities. To address these problems, we first propose a neural-network-based zero pronoun resolver (NZR) that is capable of generating vector-space semantics of zero pronouns and candidate antecedents. On the basis of NZR, we develop the collaborative filtering-based framework for Chinese zero pronoun resolution task, exploring the reciprocities between the candidate antecedents of a zero pronoun to more rationally re-estimate their importance. Experimental results on the Chinese portion of the OntoNotes 5.0 corpus are encouraging: Our proposed model substantially surpasses the Chinese zero pronoun resolution baseline systems.

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    • Published in

      cover image ACM Transactions on Asian and Low-Resource Language Information Processing
      ACM Transactions on Asian and Low-Resource Language Information Processing  Volume 19, Issue 1
      January 2020
      345 pages
      ISSN:2375-4699
      EISSN:2375-4702
      DOI:10.1145/3338846
      Issue’s Table of Contents

      Copyright © 2019 ACM

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 5 June 2019
      • Accepted: 1 April 2019
      • Revised: 1 November 2018
      • Received: 1 June 2018
      Published in tallip Volume 19, Issue 1

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