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

  • Sonia Vázquez
  • Zornitsa Kozareva
  • Andrés Montoyo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4827)


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.


Mutual Information Target Word Semantic Information Latent Semantic Analysis Word Sense 
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 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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