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Erschienen in:
2013 | OriginalPaper | Buchkapitel
GenDesc: A Partial Generalization of Linguistic Features for Text Classification
verfasst von : Guillaume Tisserant, Violaine Prince, Mathieu Roche
Erschienen in: Natural Language Processing and Information Systems
Verlag: Springer Berlin Heidelberg
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This paper presents an application that belongs to automatic classification of textual data by supervised learning algorithms. The aim is to study how a better textual data representation can improve the quality of classification. Considering that a word meaning depends on its context, we propose to use features that give important information about word contexts. We present a method named
GenDesc
, which generalizes (with POS tags) the least relevant words for the classification task.