Accurate Semantic Class Classifier for Coreference Resolution

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.

• Type: Journal
• Year: 2009
• Where: EMNLP
• Authors: Zhiheng Huang, Guangping Zeng, Weiqun Xu, Asli Çelikyilmaz
• Keywords: Accurate Sc Classifier, EMNLP 2009, Natural Language Processing, SC Agreement, SC Classification Errors