关键词: 知网, 概念语义场, 义原抽取, 概念向量, 文本聚类

Abstract: The text clustering algorithm based on traditional keyword does not take into account the semantic relation between key words，and then causes the concept of the text vector is not accurate enough.The paper proposes the text clustering algorithm based on concept vector.The algorithm adopts HowNet properties and the density of semantic field and the weight of meaning in concept tree to select the appropriate meaning of the original concepts as keywords，the text vector would be transformed from keyword vector to concept vector.It not only adds the texts semantic，but also reduces vector dimensions.It is used to realize text clustering to increase the efforts clustering.Experimental results show that the algorithm improves the accuracy and recall of text clustering.

Key words: HowNet, concept semantic field, the original meaning extracting, concept vector, text clustering