Earticle

초록

영어

Word sense disambiguation is important for many applications in natural language processing fields including machine translation, information retrieval and automatic summarization. In this paper, left word unit and right word unit are extracted for improving the quality of word sense disambiguation (WSD) starting from the target polysemous word. Their semantic knowledge is mined from Tongyici Cilin which is a Chinese semantic lexicon. A new method of word sense disambiguation is proposed with semantic information of left word unit and right word unit. The classifier of word sense disambiguation is built based on bayesian model. SemEval-2007: Task#5 is used as training corpus and test corpus. Experimental results show that the disambiguation classifier’s precision is improved and demonstrate the effectiveness of the method.

목차

Abstract
 1. Introduction
 2. Extracting Discriminative Features for WSD
 3. Bayesian Classifier based on Semantic Knowledge
 4. Experiments
 5. Conclusion
 Acknowledgements
 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 Lu-Rong [ School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China ]
• Gao Xue-Ya [ 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 ]
• Yue Yong [ Department of Computer Science and Technology, University of Bedfordshire, Park Square, Luton LU1 3JU, United Kingdom ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605