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| Research on Chinese Text Categorization Based on Semantic Similarity of HowNet |
| Liu Huailiang, Du Kun, Qin Chunxiu |
| School of Economics & Management, Xidian University, Xi'an 710126, China |
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Abstract [Objective] This is an algorithm for improving the classification precision of Chinese text classification, which can calculate the similarity between Chinese texts more accurately. [Methods] With the TF-IDF algorithm calculating item weight and HowNet analyzing the semantic relationships between lexical items, this paper proposes a text similarity weighting algorithm based on HowNet semantics similarity, and makes an experiment on its Chinese text classification. [Results] The experiment resualts show that the proposed method can improve the text categorization performance comparing with the traditional ones. [Limitations] This algorithm is quite high in its time complexity, and its speed of text classification needs to be improved. [Conclusions] It is proved to be an effective algorithm for enhancing the classification accuracy of Chinese text by analyzing the semantic relationships between feature items.
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Received: 22 September 2014
Published: 17 March 2015
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