The traditional text classification algorithms has the problems of high-dimensional, rarefaction and ignoring the semantic correlation of keywords in the vector space model, and it easily leads to low efficiency and poor quality. Taking HowNet as knowledge repository, this paper develops the semantic concept vector model to represent text, merges synonyms and disambiguates polymerizes according to the concept of semantic and the context background. Then it proposes the text classification algorithm of TCABCC based on concept clusters by improving KNN, which uses concept clusters to present training samples of each category, makes similarity calculation based on text concept vector and category concept clusters. The experimental results show that the classifier constructed by this algorithm greatly improves the efficiency and performance than traditional KNN.
Ma Jialin
,
Liu Jinling
,
Jin Chunxia
. Text Classification Algorithm Based on Concept Clusters[J]. Library and Information Service, 2013
, 57(15)
: 132
-136,82
.
DOI: 10.7536/j.issn.0252-3116.2013.15.021
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