基于子树匹配的文本相似度算法

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Abstract To reduce the dimensionality of text vectors and improve the performance of semantic similarity measurement,an algorithm for texts similarity computation is proposed,which combines the advantages of the statistical methods and semantic dictionary. The texts are utilized to generate metadata feature vectors,so that it reduces the dimensionality of text vectors space. The algorithm for computing texts similarity is designed based on subtrees matching and the speed of computing texts similarity is improved. The accuracy of texts semantic similarity measurement is improved by utilizing the semantic matching of metadata feature vectors and subtrees. The synonyms widely existing in metadata are processed by the proposed method,and the semantic coverage ability for similarity computation of texts is also enhanced. The experimental results show that the proposed method is feasible and effective.

Received: 06 May 2013

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	Articles by authors
	ZHANG Pei-Yun
	CHEN Chuan-Ming
	HUANG Bo

Cite this article:

ZHANG Pei-Yun,CHEN Chuan-Ming,HUANG Bo. Texts Similarity Algorithm Based on Subtrees Matching[J]. , 2014, 27(3): 226-234.

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http://manu46.magtech.com.cn/Jweb_prai/EN/ OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2014/V27/I3/226

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