Integrating WordNet knowledge to supplement training data in semi-supervised agglomerative hierarchical clustering for text categorization.

TitreIntegrating WordNet knowledge to supplement training data in semi-supervised agglomerative hierarchical clustering for text categorization.
Publication TypeJournal Article
Year of Publication2001
AuthorsBenkhalifa, M, Mouradi, A, Bouyakhf, H
JournalInternational Journal of Intelligent Systems
Volume16
Pagination929 - 947
ISSN08848173
Mots-clésAlgorithms, Artificial intelligence, Hierarchy (Linguistics), John Wiley & Sons Inc., Linguistic analysis (Linguistics), Machine learning
Abstract

The text categorization (TC) is the automated assignment of text documents to predefined categories based on document contents. TC has been an application for many learning approaches, which proved effective. Nevertheless, TC provides many challenges to machine learning. In this paper, we suggest, for text categorization, the integration of external WordNet lexical information to supplement training data for a semi-supervised clustering algorithm which (i) uses a finite design set of labeled data to (ii) help agglomerative hierarchical clustering algorithms (AHC) partition a finite set of unlabeled data and then (iii) terminates without the capacity to classify other objects. This algorithm is the “semi-supervised agglomerative hierarchical clustering algorithm” (ssAHC). Our experiments use Reuters 21578 database and consist of binary classifications for categories selected from the 89 TOPICS classes of the Reuters collection. Using the vector space model (VSM), each document is repre

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