Computer Science ›› 2016, Vol. 43 ›› Issue (2): 239-244.doi: 10.11896/j.issn.1002-137X.2016.02.050

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Liheci Word Sense Disambiguation Based on SVM

ZHANG Zhen-jing, LI Xin-fu, TIAN Xue-dong and WANG Kai   

  • Online:2018-12-01 Published:2018-12-01

Abstract: The task of Liheci word sense disambiguation is to make computers choose the correct sense of a Liheci ambiguous word in a given context.For the problem that a Liheci ambiguous word in machine translation is not accurate and in the information retrieval is unable to match the useful information,a word sense disambiguation method was applied to the Liheci ambiguous words and a classifier model was established using SVM.In order to improve the accuracy of the Liheci word sense disambiguation,it extracts not only local word,local part of speech,local word and part of speech,but also the middle insert part of the separated form as disambiguation features according to the characteristics of Liheci.When the text characteristics was converted to feature vector,we could fixed feature weights of some type in turn and changed the feature weights of the other two types to verity the disambiguation effect of the three kinds of feature,respectively.The results show that the effect of local word feature,local word and part of speech features on disambiguation is higher than local part of speech,and using different types of feature weight compared with the same,disambiguation accuracy increases by 1.03%~5.69%.

Key words: Liheci,SVM,Word sense disambiguation,Classifier

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