Detecting Deviations in Text Collections: An Approach Using Conceptual Graphs

  • M. Montes-y-Gómez
  • A. Gelbukh
  • A. López-López
Conference paper

DOI: 10.1007/3-540-46016-0_19

Part of the Lecture Notes in Computer Science book series (LNCS, volume 2313)
Cite this paper as:
Montes-y-Gómez M., Gelbukh A., López-López A. (2002) Detecting Deviations in Text Collections: An Approach Using Conceptual Graphs. In: Coello Coello C.A., de Albornoz A., Sucar L.E., Battistutti O.C. (eds) MICAI 2002: Advances in Artificial Intelligence. MICAI 2002. Lecture Notes in Computer Science, vol 2313. Springer, Berlin, Heidelberg

Abstract

Deviation detection is an important problem of both data and text mining. In this paper we consider the detection of deviations in a set of texts represented as conceptual graphs. In contrast with statistical and distance-based approaches, the method we propose is based on the concept of generalization and regularity. Among its main characteristics are the detection of rare patterns (that attempt to give a generalized description of rare texts) and the ability to discover local deviations (deviations at different contexts and generalization levels). The method is illustrated with the analysis of a set of computer science papers.

Keywords

natural language processing deviation detection text mining conceptual graphs regularity 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • M. Montes-y-Gómez
    • 1
    • 2
  • A. Gelbukh
    • 1
  • A. López-López
    • 2
  1. 1.Center for Computing Research (CIC-IPN)México
  2. 2.Instituto Nacional de Astrofísica, Optica y Electrónica (INAOE)México

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