Research on Feature Weights of Liheci Word Sense Disambiguation

Concerning the problems of the comparative translation is not accurate in machine translation and the useful information is unable to match in information retrieval, a liheci word sense disambiguation method is adopted and a classifier model is established using Support Vector Machines(SVM). To improve the accuracy of the liheci word sense disambiguation, it extracts not only local words(LW), local part-of-speeches(POS), local words and part-of-speeches (LWP) but also the middle insert part of the separated form as disambiguation features according to the characteristics of liheci, When the text features are converted to feature vectors for improving on boolean weight method, we can fix feature weights of some type in turn and change the other two types´ to verify the disambiguation effect of three kinds of features, respectively. The results show that the effect of LW, LWP is higher than POS. Setting higher feature weights to LW and LWP, the disambiguation accuracy can effectively improve.