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Improving Term Extraction by System Combination Using Boosting

  • Jordi Vivaldi
  • 2Lluís Màrquez
  • Horacio Rodríguez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2167)

Abstract

Term extraction is the task of automatically detecting, from textual corpora, lexical units that designate concepts in thematically restricted domains (e.g. medicine). Current systems for term extraction integrate linguistic and statistical cues to perform the detection of terms. The best results have been obtained when some kind of combination of simple base term extractors is performed [14]. In this paper it is shown that this combination can be further improved by posing an additional learning problem of how to find the best combination of base term extractors. Empirical results, using AdaBoost in the metalearning step, show that the ensemble constructed surpasses the performance of all individual extractors and simple voting schemes, obtaining significantly better accuracy figures at all levels of recall.

Keywords

Medical Domain Term Candidate System Combination Weak Hypothesis AdaBoost Algorithm 
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 2001

Authors and Affiliations

  • Jordi Vivaldi
    • 1
  • 2Lluís Màrquez
  • Horacio Rodríguez
    • 2
  1. 1.Institut Universitari de Lingüística AplicadaUniversitat Pompeu FabraBarcelonaCatalonia
  2. 2.TALP Research CenterUniversitat Politècnica de CatalunyaBarcelona, Catalonia

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