Improving Text Categorization with Semantic Knowledge in Wikipedia
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- WANG Xiang
- School of Computer, National University of Defense Technology
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- JIA Yan
- School of Computer, National University of Defense Technology
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- CHEN Ruhua
- School of Computer, National University of Defense Technology
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- FAN Hua
- School of Computer, National University of Defense Technology
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- ZHOU Bin
- School of Computer, National University of Defense Technology
Description
Text categorization, especially short text categorization, is a difficult and challenging task since the text data is sparse and multidimensional. In traditional text classification methods, document texts are represented with “Bag of Words (BOW)” text representation schema, which is based on word co-occurrence and has many limitations. In this paper, we mapped document texts to Wikipedia concepts and used the Wikipedia-concept-based document representation method to take the place of traditional BOW model for text classification. In order to overcome the weakness of ignoring the semantic relationships among terms in document representation model and utilize rich semantic knowledge in Wikipedia, we constructed a semantic matrix to enrich Wikipedia-concept-based document representation. Experimental evaluation on five real datasets of long and short text shows that our approach outperforms the traditional BOW method.
Journal
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- IEICE Transactions on Information and Systems
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IEICE Transactions on Information and Systems E96.D (12), 2786-2794, 2013
The Institute of Electronics, Information and Communication Engineers
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Details 詳細情報について
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- CRID
- 1390282679355953152
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- NII Article ID
- 130003385449
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- ISSN
- 17451361
- 09168532
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- Text Lang
- en
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- Data Source
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- JaLC
- Crossref
- CiNii Articles
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- Abstract License Flag
- Disallowed