Semantic Class Induction and Coreference Resolution

Vincent Ng.
45th Annual Meeting of the Association for Computational Linguistics (ACL-07), 2007.

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Abstract

This paper examines whether a learning-based coreference resolver can be improved using semantic class knowledge that is automatically acquired from a version of the Penn Treebank in which the noun phrases are labeled with their semantic classes. Experiments on the ACE test data show that a resolver that employs such induced semantic class knowledge yields a statistically significant improvement of 2% in F-measure over one that exploits heuristically computed semantic class knowledge. In addition, the induced knowledge improves the accuracy of common noun resolution by 2-6%.