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Text clustering to help knowledge acquisition from documents

  • Stéphane Lapalut
Eliciting Knowledge from Textual and Other Sources
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1076)

Abstract

At the earlier stage of the knowledge acquisition process, interviews of experts produce a large amount of rich but ill-structured texts. Knowledge engineers need some tool to help them in the exploitation of all these texts. We propose the use of a statistical method, the top-down hierarchical classification and a new interpretation of its results. The initial statistical analysis proposed by M. Reinert [16, 17] gives two kinds of results: first a segmentation of texts that reflects their “semantic contexts” that we use to raise structures of texts, and second, classes of significant terms belonging to these contexts, which can be related to the experts or to these specialities. In this paper, we describe the method, its empirical validity and a comparison with similar approaches, its uses with examples and results. We conclude with some research directions to extend the exploitation of the analysis results.

Keywords

Knowledge Acquisition Semantic Context Knowledge Engineer Text Corpus Expository Text 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Stéphane Lapalut
    • 1
  1. 1.Projet ACACIA, INRIA Sophia AntipolisSophia Antipolis CedexFrance

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