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Textual Energy of Associative Memories: Performant Applications of Enertex Algorithm in Text Summarization and Topic Segmentation

  • Silvia Fernández
  • Eric SanJuan
  • Juan Manuel Torres-Moreno
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4827)

Abstract

In this paper we present a Neural Network approach, inspired by statistical physics of magnetic systems, to study fundamental problems of Natural Language Processing (NLP). The algorithm models documents as neural network whose Textual Energy is studied. We obtained good results on the application of this method to automatic summarization and Topic Segmentation.

Keywords

Automatic Summarization Topic Segmentation Statistical Methods Statistical Physics 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Silvia Fernández
    • 1
    • 2
  • Eric SanJuan
    • 1
  • Juan Manuel Torres-Moreno
    • 1
    • 3
  1. 1.Laboratoire Informatique d’Avignon, BP 1228 F-84911 Avignon Cedex 9France
  2. 2.Laboratoire de Physique des Matériaux, CNRS UMR 7556, NancyFrance
  3. 3.École Polytechnique de Montréal - Département de génie informatique, CP 6079 Succ. Centre Ville H3C 3A7, Montréal (Québec)Canada

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