Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/75282
Title: Use of linguistic features in context-sensitive text classification
Authors: Wong, AKS 
Lee, JWT 
Yeung, DS 
Issue Date: 2006
Publisher: Springer
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2006, v. 3930 LNAI, p. 701-710 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
Abstract: Many popular Text Classification (TC) models use simple occurrence or words in a document as features to base their classifications. They commonly assume word occurrences to be statistically independent in their design. Although such assumption does not hold in general. these TC models arc robust and efficient in their task. Some recent studies have shown contextsensitive TC approaches were able to perform better in general. On the other hand, although complex linguistic or semantic features may intuitively be more relevant in TC. studies on their effectiveness have produced mixed and inconclusive results. In this pajier. we present out investigation on the use of some complex linguistic features with two context-sensitive TC methods. Our experimental results show potential advantages of such approach.
Description: 4th International Conference on Machine Learning and Cybernetics, ICMLC 2005, Guangzhou, 18-21 August 2005
URI: http://hdl.handle.net/10397/75282
ISBN: 3540335846
9783540335849
ISSN: 0302-9743
EISSN: 1611-3349
DOI: 10.1007/11739685_73
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