Chinese Event Coreference Resolution: An Unsupervised Probabilistic Model Rivaling Supervised Resolvers

Chen Chen and Vincent Ng.
Proceedings of Human Language Technologies: The 2015 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 1097-1107, 2015.

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Abstract

Recent work has successfully leveraged the semantic information extracted from lexical knowledge bases such as WordNet and FrameNet to improve English event coreference resolvers. The lack of comparable resources in other languages, however, has made the design of high-performance non-English event coreference resolvers, particularly those employing unsupervised models, very difficult. We propose a generative model for the under-studied task of Chinese event coreference resolution that rivals its supervised counterparts in performance when evaluated on the ACE 2005 corpus.

BibTeX entry

@InProceedings{Chen+Ng:15b,
  author = {Chen Chen and Vincent Ng},
  title = {Chinese Event Coreference Resolution: An Unsupervised Probabilistic Model Rivaling Supervised Resolvers},
  booktitle = {Proceedings of Human Language Technologies: The 2015 Annual Conference of the North American Chapter of the Association for Computational Linguistics},
  pages = {1097--1107}, 
  year = 2015}