• ISSN 0258-2724
  • CN 51-1277/U
  • EI Compendex
  • Scopus
  • Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
  • Chinese S&T Journal Citation Reports
  • Chinese Science Citation Database
Volume 54 Issue 2
Jun.  2019
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Article Contents
ZHANG Chunxiang, XU Zhifeng, GAO Xueyao. Semi-Supervised Method for Chinese Word Sense Disambiguation[J]. Journal of Southwest Jiaotong University, 2019, 54(2): 408-414. doi: 10.3969/j.issn.0258-2724.20170178
Citation: ZHANG Chunxiang, XU Zhifeng, GAO Xueyao. Semi-Supervised Method for Chinese Word Sense Disambiguation[J]. Journal of Southwest Jiaotong University, 2019, 54(2): 408-414. doi: 10.3969/j.issn.0258-2724.20170178

Semi-Supervised Method for Chinese Word Sense Disambiguation

doi: 10.3969/j.issn.0258-2724.20170178
  • Received Date: 14 Mar 2017
  • Rev Recd Date: 08 Jan 2018
  • Available Online: 06 Mar 2018
  • Publish Date: 01 Apr 2019
  • To solve the problem of a word having multiple meanings in the natural language processing (NLP) field, a semi-supervised disambiguation method, that uses a range of word sense disambiguation (WSD) models and linguistic knowledge has been proposed in this paper. First, words, parts of speech and translations were used as discriminative features, which were extracted from word units adjacent to the left and right of an ambiguous word. A word sense classifier was constructed using a Bayes model, following which a word sense classifier based on a maximum entropy (ME) model was constructed. Second, a Co-Training algorithm, based on a multitude of unannotated corpora, was adopted to optimize the WSD model. Third, optimization experiments were conducted in which training corpus in SemEval-2007: Task#5 and a large number of unannotated corpora from Harbin Institute of Technology were applied to optimize the Bayesian classifier and the maximum entropy classifier. Finally, the optimized WSD model was tested. Test results demonstrate an increase in the disambiguation accuracy of the proposed method by 0.9% compared to WSD models based on support vector machines, thereby exhibiting an improvement in WSD performance.

     

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