Improving Pronoun Resolution Using Statistics-Based Semantic Compatibility Information

Proceedings of the 43rd Annual Meeting of the ACL, pages 165–172, Ann Arbor, June 2005. c⃝2005 Association for Computational Linguistics Improving Pronoun Resolution Using Statistics-Based Semantic Compatibility Information Xiaofeng Yang†‡ Jian Su† Chew Lim Tan‡ †Institute for Infocomm Research 21 Heng Mui Keng Terrace, Singapore, 119613 {xiaofengy,sujian}@i2r.a-star.edu.sg ‡ Department of Computer Science National University of Singapore, Singapore, 117543 {yangxiao,tancl}@comp.nus.edu.sg Abstract 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. 1 Introduction Semantic compatibility is an important factor for pronoun resolution. Since pronouns, especially neu- tral pronouns, carry little semantics of their own, the compatibility between an anaphor and its an- tecedent candidate is commonly evaluated by ex- amining the relationships between the candidate and the anaphor’s context, based on the statistics that the corresponding predicate-argument tuples occur in a particular large corpus. Consider the example given in the work of Dagan and Itai (1990): (1) They know full well that companies held tax money aside for collection later on the basis that the government said it1 was going to col- lect it2. For anaphor it1, the candidate government should have higher semantic compatibility than money be- cause government collect is supposed to occur more frequently than money collect in a large corpus. A similar pattern could also be observed for it2. So far, the corpus-based semantic knowledge has been successfully employed in several anaphora res- olution systems. Dagan and Itai (1990) proposed a heuristics-based approach to pronoun resolu- tion. It determined the preference of candidates based on predicate-argument frequencies. Recently, Bean and Riloff (2004) presented an unsupervised approach to coreference resolution, which mined the co-referring NP pairs with similar predicate- arguments from a large corpus using a bootstrapping method. However, the utility of the corpus-based se- mantics for pronoun resolution is often argued. Kehler et al. (2004), for example, explored the usage of the corpus-based statistics in supervised learning based systems, and found that such infor- mation did not produce apparent improvement for the overall pronoun resolution. Indeed, existing learning-based approaches to anaphor resolution have performed reasonably well using limited and shallow knowledge (e.g., Mitkov (1998), Soon et al. (2001), Strube and Muller (2003)). Could the relatively noisy semantic knowledge give us further system improvement? In this paper we focus on improving pronominal anaphora resolution using automatically computed semantic compatibility information. We propose to enhance the utility of the statistics-based knowledge from two aspects: Statistics source. Corpus-based knowledge usu- ally suffers from data sparseness problem. That is, many …

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