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Reducing Information Variation in Text

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Text- and Speech-Triggered Information Access

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2705))

Abstract

We discuss the nature and the scope of linguistic (morphological, syntactic and semantic) variation of terms and its impact on two information retrieval tasks: term acquisition and automatic indexing. A review of natural language processing techniques existing in these two areas is done, along with an in-depth presentation of FASTR, a corpus processor for the recognition, normalization, and acquisition of multi-word terms.

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Authors and Affiliations

  1. LADL, IGM, Université Marne-la-Vallée, 5, bd Descartes, Champs-sur-Marne, 77454, Marne-la-Vallée, France

    Agata Savary

  2. LIMSI-CNRS, BP 133, 91403, Orsay, France

    Christian Jacquemin

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  1. Agata Savary
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  2. Christian Jacquemin
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Editors and Affiliations

  1. University of Edinburgh, Edinburgh, Scotland

    Steve Renals

  2. CEA-LIST, Fontenay-aux-Roses, France

    Gregory Grefenstette

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Savary, A., Jacquemin, C. (2003). Reducing Information Variation in Text. In: Renals, S., Grefenstette, G. (eds) Text- and Speech-Triggered Information Access. Lecture Notes in Computer Science(), vol 2705. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45115-0_6

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  • DOI: https://doi.org/10.1007/978-3-540-45115-0_6

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