chen-keywordextractionfortext-2005.pdf (1.04 MB)
Keyword extraction for text categorization
conference contribution
posted on 2005-01-01, 00:00 authored by Jiyuan An, Yi-Ping Phoebe ChenText categorization (TC) is one of the main applications of machine learning. Many methods have been proposed, such as Rocchio method, Naive bayes based method, and SVM based text classification method. These methods learn labeled text documents and then construct a classifier. A new coming text document's category can be predicted. However, these methods do not give the description of each category. In the machine learning field, there are many concept learning algorithms, such as, ID3 and CN2. This paper proposes a more robust algorithm to induce concepts from training examples, which is based on enumeration of all possible keywords combinations. Experimental results show that the rules produced by our approach have more precision and simplicity than that of other methods.
History
Event
Active Media Technology. Conference (2005: Kagawa, Japan)Pagination
556 - 561Publisher
IEEELocation
Kagawa, JapanPlace of publication
Piscataway, N.J.Start date
2005-05-19End date
2005-05-21ISBN-13
9780780390355ISBN-10
0780390350Language
engNotes
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holderPublication classification
E1 Full written paper - refereedCopyright notice
2005 IEEE.Editor/Contributor(s)
H Tarumi, Y Li, T YoshidaTitle of proceedings
Proceedings of the 2005 International Conference on Active Media TechnologyUsage metrics
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