Information Extraction and Classification from Free Text Using a Neural Approach

  • Ignazio Gallo
  • Elisabetta Binaghi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4756)

Abstract

Many approaches to Information Extraction (IE) have been proposed in literature capable of finding and extract specific facts in relatively unstructured documents. Their application in a large information space makes data ready for post-processing which is crucial to many context such as Web mining and searching tools. This paper proposes a new IE strategy, based on symbolic and neural techniques, and tests it experimentally within the price comparison service domain. In particular the strategy seeks to locate a set of atomic elements in free text which is preliminarily extracted from web documents and subsequently classify them assigning a class label representing a specific product.

Keywords

Information Extraction Neural Network Text Classification 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Ignazio Gallo
    • 1
  • Elisabetta Binaghi
    • 1
  1. 1.Department of Computer Science and Communication, Universitá degli Studi dell’Insubria, via Mazzini 5, VareseItaly

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