In this paper we focus on how to improve pronoun resolution using the statisticsbased semantic compatibility information. We investigate two unexplored issues that influence the effectiveness of such information: 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 compatibility obtained from the web can be effectively incorporated in the twin-candidate learning model and significantly improve the resolution of neutral pronouns.
REF use statistically-based semantic compatibility information to improve pronoun resolution.
Improving Pronoun Resolution Using Statistics-Based Semantic Compatibility Information
Annual Meeting Of The Association For Computational Linguistics
Computer Science