Boosting for Text Classification with Semantic Features

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...

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Bibliographic Details
Published inAdvances in Web Mining and Web Usage Analysis pp. 149 - 166
Main Authors Bloehdorn, Stephan, Hotho, Andreas
Format Book Chapter
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2006
SeriesLecture Notes in Computer Science
Subjects
Feature Representation
Lexical Entry
Noun Phrase
Semantic Feature
Weak Learner
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ISBN3540471278
9783540471271
ISSN0302-9743
1611-3349
DOI10.1007/11899402_10

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Summary: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.
ISBN:3540471278
9783540471271
ISSN:0302-9743
1611-3349
DOI:10.1007/11899402_10