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Mining Textual Data through Term Variant Clustering : the TermWatch system

Abstract : We present a system for mapping the structure of research topics in a corpus. TermWatch portrays the "aboutness" of a corpus of scientific and technical publications by bridging the gap between pure statistical approaches and symbolic techniques. In the present paper, an experiment on unsupervised textmining is performed on a corpus of scientific titles and abstracts from 16 prominent IR journals. The preliminary results showed that TermWatch was able to capture low occurring phenomena which the usual clustering methods based on co-occurrence may not highlight. The results also reflect the expressive power of terminological variations as a means to capture the structure of research topics contained in a corpus.
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https://archivesic.ccsd.cnrs.fr/sic_00001405
Contributor : Fidelia Ibekwe <>
Submitted on : Sunday, April 10, 2005 - 2:48:02 PM
Last modification on : Thursday, May 28, 2020 - 7:52:02 PM
Long-term archiving on: : Saturday, April 3, 2010 - 9:50:22 PM

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Fidelia Ibekwe-Sanjuan, Eric Sanjuan. Mining Textual Data through Term Variant Clustering : the TermWatch system. Recherche d'Information Assistée par Ordinateur (RIAO 2004)., 2004. ⟨sic_00001405⟩

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