How Context and Semantic Information Can Help a Machine Learning System?

  • Sonia Vázquez
  • Zornitsa Kozareva
  • Andrés Montoyo
Conference paper

DOI: 10.1007/978-3-540-76631-5_95

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4827)
Cite this paper as:
Vázquez S., Kozareva Z., Montoyo A. (2007) How Context and Semantic Information Can Help a Machine Learning System?. In: Gelbukh A., Kuri Morales Á.F. (eds) MICAI 2007: Advances in Artificial Intelligence. MICAI 2007. Lecture Notes in Computer Science, vol 4827. Springer, Berlin, Heidelberg

Abstract

In Natural Language Processing there are different problems to solve: lexical ambiguity, summarization, information extraction, speech processing, etc. In particular, lexical ambiguity is a difficult task that nowadays is still open to new approaches. In fact, there is still a lack of systems that solve efficiently this kind of problem. At present, we find two different approaches: knowledge systems and machine learning systems. Recent studies demonstrate that machine learning systems obtain better results than knowledge systems but there is a problem: the lack of annotated contexts and corpus to train the systems. In this work, we try to avoid this situation by combining a new machine learning system with a knowledge based system.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Sonia Vázquez
    • 1
  • Zornitsa Kozareva
    • 1
  • Andrés Montoyo
    • 1
  1. 1.Departamento de Lenguajes y Sistemas Informáticos, Universidad de Alicante 

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