Incorporating coreference resolution into word sense disambiguation

  • Authors:

  • Shangfeng Hu;Chengfei Liu

  • Affiliations:

  • Faculty of Information and Communication Technologies, Swinburne University of Techonolgy, Hawthorn, VIC, Australia;Faculty of Information and Communication Technologies, Swinburne University of Techonolgy, Hawthorn, VIC, Australia

  • Venue:
  • CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part I
  • Year:
  • 2011

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Abstract

Word sense disambiguation (WSD) and coreference resolution are two fundamental tasks for natural language processing. Unfortunately, they are seldom studied together. In this paper, we propose to incorporate the coreference resolution technique into a word sense disambiguation system for improving disambiguation precision. Our work is based on the existing instance knowledge network (IKN) based approach for WSD. With the help of coreference resolution, we are able to connect related candidate dependency graphs at the candidate level and similarly the related instance graph patterns at the instance level in IKN together. Consequently, the contexts which can be considered for WSD are expanded and precision for WSD is improved. Based on Senseval-3 all-words task, we run extensive experiments by following the same experimental approach as the IKN based WSD. It turns out that each combined algorithm between the extended IKN WSD algorithm and one of the best five existing algorithms consistently outperforms the corresponding combined algorithm between the IKN WSD algorithm and the existing algorithm.