关键词: 文本聚类, 概念, 文本表示, 小世界理论, 语义相似度

Abstract: This paper introduces a new document clustering method using concept and semantic similarity—Text Clustering Based on Concept and Semantic Similarity（TCBCSS）. Key concept is extracted, instead of the keyword, to form semantic network. The semantic network is analyzed using Six Degrees of Separation and geometric characteristics, to build concept lists, which represent the document. This not only resolves the problem of differentially expressed, but also is more convenient for similarity computation. TCBCSS algorithm uses semantic similarity of concept lists as a measure of similarity between the two documents, and clusters the document based on graph, to avoid some?limitations?of?the?clustering algorithm?on?the?clustered shape. Experimental results prove that TCBCSS algorithm improves the quality of the clustering.

Key words: text clustering, concept, text representation, Six Degrees of Separation, semantic similarity