Abstract: Research on word similarity measurement has been popular not only in natural language processing but also in other basic research．Traditional word similarity measurements use semantic lexical or large-scale corpus．We first discussed the background of the applications of word similarity measurement,such as information retrieval,information extraction,text classification,example-based machine translation,etc．Then two strategies of word similarity measurement were summarized:one is based on ontology or a semantic taxonomy,the other is based on large collocations of words in corpus．BaiduBaike,an online open encyclopedia,could be used not only as a corpus but also a knowledge resource with rich semantic information．Based on BaiduBaike with its rich semantic information and category graph,we proposed a new method to analyze and compute Chinese word similarity from four dimensions:the baike card,the content of word,the open classification of word and the correlation words．We used language-network to choose top key terms of content of word．Based on vector space mode (VSM) theory,we calculated the similarity between parts of words．We presented a new “multi-path searching” algorithm on BaiduBaike category graph．A comprehensive similarity measuring method based on the four parts was proposed．Experiment results show that the method has a good performance.

Key words: Word similarity,Language network,BaiduBaike,VSM