Int J Performability Eng ›› 2019, Vol. 15 ›› Issue (5): 1491-1498.doi: 10.23940/ijpe.19.05.p26.14911498

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Word Sense Disambiguation based on Maximum Entropy Classifier

Chunxiang Zhanga, Xuesong Zhoub, Xueyao Gaob, *, and Bo Yua   

  1. a School of Software and Microelectronics, Harbin University of Science and Technology, Harbin, 150080, China
    b School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, 150080, China
  • Submitted on ;
  • Contact: * E-mail address: xueyao_gao@163.com
  • Supported by:
    This work is supported by the National Natural Science Foundation of China (No. 61502124, 60903082), China Postdoctoral Science Foundation Funded Project (No. 2014M560249), Natural Science Foundation of Heilongjiang Province of China (No. F2017014, F2015041, F201420), and Scientific Research Projects of Science and Technology Talents in Harbin University of Science and Technology.

Abstract: Word sense disambiguation (WSD) is one of the most important research issues in the field of natural language processing. In this paper, a new method of word sense disambiguation is proposed, in which words and parts of speech (POS) are extracted as discriminative features. At the same time, a maximum entropy classifier is adopted to determine ambiguous words' semantic categories. Training data of SemEval-2007: Task#5 is used to optimize the maximum entropy model. A test corpus is applied to test the performance of the WSD classifier. Experimental results show that the performance of word sense disambiguation is improved after the proposed approach is used.

Key words: word sense disambiguation, natural language processing, discriminative features, maximum entropy classifier, semantic category