Earticle

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

저자

• 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 ] Corresponding author
• Lu Zhi-Mao [ School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605