Semantic Class Induction and Coreference Resolution

Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pages 536–543, Prague, Czech Republic, June 2007. c⃝2007 Association for Computational Linguistics Semantic Class Induction and Coreference Resolution Vincent Ng Human Language Technology Research Institute University of Texas at Dallas Richardson, TX 75083-0688 vince@hlt.utdallas.edu Abstract This paper examines whether a learning- based coreference resolver can be improved using semantic class knowledge that is au- tomatically acquired from a version of the Penn Treebank in which the noun phrases are labeled with their semantic classes. Ex- periments on the ACE test data show that a resolver that employs such induced semantic class knowledge yields a statistically signif- icant improvement of 2% in F-measure over one that exploits heuristically computed se- mantic class knowledge. In addition, the in- duced knowledge improves the accuracy of common noun resolution by 2-6%. 1 Introduction In the past decade, knowledge-lean approaches have significantly influenced research in noun phrase (NP) coreference resolution — the problem of deter- mining which NPs refer to the same real-world en- tity in a document. In knowledge-lean approaches, coreference resolvers employ only morpho-syntactic cues as knowledge sources in the resolution process (e.g., Mitkov (1998), Tetreault (2001)). While these approaches have been reasonably successful (see Mitkov (2002)), Kehler et al. (2004) speculate that deeper linguistic knowledge needs to be made avail- able to resolvers in order to reach the next level of performance. In fact, semantics plays a crucially im- portant role in the resolution of common NPs, allow- ing us to identify the coreference relation between two lexically dissimilar common nouns (e.g., talks and negotiations) and to eliminate George W. Bush from the list of candidate antecedents of the city, for instance. As a result, researchers have re-adopted the once-popular knowledge-rich approach, investi- gating a variety of semantic knowledge sources for common noun resolution, such as the semantic rela- tions between two NPs (e.g., Ji et al. (2005)), their semantic similarity as computed using WordNet (e.g., Poesio et al. (2004)) or Wikipedia (Ponzetto and Strube, 2006), and the contextual role played by an NP (see Bean and Riloff (2004)). Another type of semantic knowledge that has been employed by coreference resolvers is the se- mantic class (SC) of an NP, which can be used to dis- allow coreference between semantically incompat- ible NPs. However, learning-based resolvers have not been able to benefit from having an SC agree- ment feature, presumably because the method used to compute the SC of an NP is too simplistic: while the SC of a proper name is computed fairly accu- rately using a named entity (NE) recognizer, many resolvers simply assign to a common noun the first (i.e., most frequent) WordNet sense as its SC (e.g., Soon et al. (2001), Markert and Nissim (2005)). It is not easy to measure the accuracy of this heuristic, but the fact that the SC agreement feature is not used by Soon et al.’s decision tree coreference classifier seems to suggest that the SC values of the NPs are …

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