Boosting for Text Classification with Semantic Features

Abstract. Current text classification systems typically use term stems for representing document content. Semantic Web technologies allow the usage of features on a higher semantic level than single words for text classification purposes. In this paper we propose such an enhancement of the classical document representation through concepts extracted from background knowledge. Boosting, a successful machine learning technique is used for classification. Comparative experimental evaluations in three different settings support our approach through consistent improvement of the results. An analysis of the results shows that this improvement is due to two separate effects.

• Type: Conference
• Year: 2004
• Where: KDD
• Authors: Stephan Bloehdorn, Andreas Hotho
• Keywords: Current Text Classification, Data Mining, KDD 2004, Semantic Web Technologies, Text Classification Purposes