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논문검색

Chinese Word Sense Disambiguation Based on Hidden Markov Model

원문정보

Zhang Chun-Xiang, Sun Yan-Chen, Gao Xue-Yao, Lu Zhi-Mao

보안공학연구지원센터(IJDTA) International Journal of Database Theory and Application Vol.8 No.6 2015.12 pp.263-270
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초록

영어

Word sense disambiguation (WSD) is important for natural language processing. It plays important roles in information retrieval, machine translation, text categorization and topic tracking. In this paper, the transition among senses of words is considered. For an ambiguous word, its semantic codes and its left word’s semantic codes are taken as disambiguation features. At the same time, a new method based on hidden Markov model (HMM) is proposed for Chinese word sense disambiguation. Chinese Tongyici Cilin is used to determine semantic codes of words. HMM is optimized in training corpus. The WSD classifiers based on HMM is tested. Experimental results show that the accuracy of word sense disambiguation is improved.

목차

Abstract
 1. Introduction
 2. Word Sense Disambiguation Based on HMM
 3. Train Model Parameters
 4. Experiment
 5. Conclusion
 References

키워드

저자정보

  • Zhang Chun-Xiang School of Software, Harbin University of Science and Technology, Harbin 150080, China, College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
  • Sun Yan-Chen School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China
  • Gao Xue-Yao School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China
  • Lu Zhi-Mao School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China

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