The Algorithm of Sense Disambiguation Based on Bayesian Model

Abstract:

Sense disambiguation is an important problem in pattern recognition. In this paper, a new algorithm of sense disambiguation is proposed, in which part-of-speech tags of the left word and the right word around the ambiguous word are extracted as discriminative features. At the same time, the bayesian model is selected as the sense disambiguation classifier and it is built based on discriminative features. The architecture of sense classification is given. The new algorithm is trained on sense-annotated corpus. Then it is used to determine its sense category. Experimental results show that the accuracy rate of disambiguation arrives at 60%.

• Periodical: Applied Mechanics and Materials (Volumes 427-429)
• Pages: 1879-1882
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.427-429.1879
• Online since: September 2013
• Authors: Chun Xiang Zhang*, Xue Yao Gao, Zhi Mao Lu
• Keywords: Bayesian Model, Discriminative Features, Sense Classification, Sense Disambiguation

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