Accurate semantic class classifier for coreference resolution

Authors:
Zhiheng Huang;Guangping Zeng;Weiqun Xu;Asli Celikyilmaz
Affiliations:
University of California at Berkeley, CA;University of California at Berkeley, CA and University of Science and Technology, Beijing, China;Chinese Academy of Sciences, Beijing, China;University of California at Berkeley, CA
Venue:
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
Year:
2009

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Abstract

There have been considerable attempts to incorporate semantic knowledge into coreference resolution systems: different knowledge sources such as WordNet and Wikipedia have been used to boost the performance. In this paper, we propose new ways to extract WordNet feature. This feature, along with other features such as named entity feature, can be used to build an accurate semantic class (SC) classifier. In addition, we analyze the SC classification errors and propose to use relaxed SC agreement features. The proposed accurate SC classifier and the relaxation of SC agreement features on ACE2 coreference evaluation can boost our baseline system by 10.4% and 9.7% using MUC score and anaphor accuracy respectively.