Dictionary-based text categorization of chemical web pages |
| |
| Authors: | Chun-Yan Liang Li GuoZhao-Jie Xia Feng-Guang NieXiao-Xia Li Liang SuZhang-Yuan Yang |
| |
| Institution: | Key Laboratory of Multiphase Reactions, Institute of Process Engineering, Chinese Academy of Sciences, P.O. Box 353, Beijing 100080, China |
| |
| Abstract: | A new dictionary-based text categorization approach is proposed to classify the chemical web pages efficiently. Using a chemistry dictionary, the approach can extract chemistry-related information more exactly from web pages. After automatic segmentation on the documents to find dictionary terms for document expansion, the approach adopts latent semantic indexing (LSI) to produce the final document vectors, and the relevant categories are finally assigned to the test document by using the k-NN text categorization algorithm. The effects of the characteristics of chemistry dictionary and test collection on the categorization efficiency are discussed in this paper, and a new voting method is also introduced to improve the categorization performance further based on the collection characteristics. The experimental results show that the proposed approach has the superior performance to the traditional categorization method and is applicable to the classification of chemical web pages. |
| |
| Keywords: | Chemistry-focused search engine Dictionary-based text categorization Automatic segmentation k-NN Latent semantic indexing Voting |
| 本文献已被 ScienceDirect 等数据库收录！ |
