Abstract: This paper proposes a method of computing lexical semantic similarity based on HowNet and search engines. The similarity computation is optimized by using the depth, density and information of semantic primitive in the hierarchy tree. The search engine based lexical semantic similarity computation is optimized by combining the point by point common information (PMI) algorithm with the normalized Google distance (NGD) algorithm. The lexical part of speech is used as weighting factor to merge the word similarity computation between HowNet and search engine. The experimental results show that, compared with the semantic similarity calculation method based on HowNet and search engine, the average similarity of the proposed method on NLPCC test set is closer to the evaluation criteria of the test set, and lexical similarity in the car ticket calculation fields has a good application effect.

Key words: semantic similarity, HowNet, sememe, search engines