Paper: Semantic Class Induction and Coreference Resolution

ACL ID P07-1068
Title Semantic Class Induction and Coreference Resolution
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2007
  • Vincent Ng (University of Texas at Dallas, Richardson TX)

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%.