Paper: Improving Pronoun Resolution Using Statistics-Based Semantic Compatibility Information

ACL ID P05-1021
Title Improving Pronoun Resolution Using Statistics-Based Semantic Compatibility Information
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2005
Authors
  • Xiaofeng Yang (Institute for Infocomm Research, Singapore; National University of Singapore, Singapore)
  • Jian Su (Institute for Infocomm Research, Singapore)
  • Chew Lim Tan (National University of Singapore, Singapore)

In this paper we focus on how to improve pronoun resolution using the statistics- based semantic compatibility information. We investigate two unexplored issues that influence the effectiveness of such in- formation: statistics source and learning framework. Specifically, we for the first time propose to utilize the web and the twin-candidate model, in addition to the previous combination of the corpus and the single-candidate model, to compute and apply the semantic information. Our study shows that the semantic compatibil- ity obtained from the web can be effec- tively incorporated in the twin-candidate learning model and significantly improve the resolution of neutral pronouns.