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Instance Pruning by Filtering Uninformative Words: An Information Extraction Case Study

  • Alfio Massimiliano Gliozzo
  • Claudio Giuliano
  • Raffaella Rinaldi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3406)

Abstract

In this paper we present a novel instance pruning technique for Information Extraction (IE). In particular, our technique filters out uninformative words from texts on the basis of the assumption that very frequent words in the language do not provide any specific information about the text in which they appear, therefore their expectation of being (part of) relevant entities is very low. The experiments on two benchmark datasets show that the computation time can be significantly reduced without any significant decrease in the prediction accuracy. We also report an improvement in accuracy for one task.

Keywords

Frequent Word Information Extraction GENIA Task Word Sense Disambiguation GENIA Corpus 
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 2005

Authors and Affiliations

  • Alfio Massimiliano Gliozzo
    • 1
  • Claudio Giuliano
    • 1
  • Raffaella Rinaldi
    • 1
  1. 1.Istituto per la Ricerca Scientifica e TecnologicaITC-irstTrentoItaly

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