Chinese Word Sense Disambiguation Based on Hidden Markov Model
Zhang Chun-xiang, Sun Yan-Chen, Xue-Yao Gao, Zhimao Lu · International Journal of Database Theory and Application · 2015
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.