Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/41236
Title: Improving text classification by a sense spectrum approach to term expansion
Authors: Wittek, P.
Darányi, S.
Tan, C.L. 
Issue Date: 2009
Source: Wittek, P.,Darányi, S.,Tan, C.L. (2009). Improving text classification by a sense spectrum approach to term expansion. CoNLL 2009 - Proceedings of the Thirteenth Conference on Computational Natural Language Learning : 183-191. ScholarBank@NUS Repository.
Abstract: Experimenting with different mathematical objects for text representation is an important step of building text classification models. In order to be efficient, such objects of a formal model, like vectors, have to reasonably reproduce language-related phenomena such as word meaning inherent in index terms. We introduce an algorithm for sense-based semantic ordering of index terms which approximates Cruse's description of a sense spectrum. Following semantic ordering, text classification by support vector machines can benefit from semantic smoothing kernels that regard semantic relations among index terms while computing document similarity. Adding expansion terms to the vector representation can also improve effectiveness. This paper proposes a new kernel which discounts less important expansion terms based on lexical relatedness. © 2009 Association for Computational Linguistics.
Source Title: CoNLL 2009 - Proceedings of the Thirteenth Conference on Computational Natural Language Learning
URI: http://scholarbank.nus.edu.sg/handle/10635/41236
ISBN: 1932432299
Appears in Collections:Staff Publications

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