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.


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  1. 1.
    Magnini, B., Strapparava, C.: Experiments in word domain disambiguation for paralell texts. In: Proceedings of SIGLEX. Workshop on Word Senses and Multi-linguality (2000)Google Scholar
  2. 2.
    Deerwester, S.C., Dumais, S.T., Landauer, T.K., Furnas, G.W., Harshman, R.A.: Indexing by latent semantic analysis. JASIS 41(6), 391–407 (1990)CrossRefGoogle Scholar
  3. 3.
    Webster, J.J., Chow, I.C.: Mapping framenet and sumo with wordnet verb: Statistical distribution of lexical-ontological realization. In: Gelbukh, A., Reyes-Garcia, C.A. (eds.) MICAI 2006. LNCS (LNAI), vol. 4293, pp. 262–268. Springer, Heidelberg (2006)Google Scholar
  4. 4.
    Kozareva, Z., Vázquez, S., Montoyo, A.: The usefulness of conceptual representation for the identification of semantic variability expressions. In: CICLing, pp. 325–336 (2007)Google Scholar
  5. 5.
    Magnini, B., Cavaglià, G.: Integrating subject field codes into wordnet. In: Gavrilidou, M., Crayannis, G., Markantonatu, S., Piperidis, S., Stainhaouer, G. (eds.) Second International Conference on Language Resources Proceedings of LREC-2000 and Greece Evaluation, Athens, pp. 1413–1418 (2000)Google Scholar
  6. 6.
    Ide, N., Veronis, J.: Introduction to the special issue on word sense disambiguation: The state of the art. In: Computational Linguistics, pp. 1–40 (1998)Google Scholar
  7. 7.
    Pedersen, T., Patwardhan, S., Michelizzi, J.: Wordnet:similarity - measuring the relatedness of concepts. In: Proceedings of the Nineteenth National Conference on Artificial Intelligence (AAAI-2004), pp. 1024–1025 (2004)Google Scholar
  8. 8.
    Saarikoski, H.M.T., Legrand, S., Gelbukh, A.F.: Defining classifier regions for wsd ensembles using word space features. In: MICAI, pp. 855–867 (2006)Google Scholar
  9. 9.
    Moldovan, D.I., Harabagiu, S.M., Miller, G.A.: Wordnet 2 - a morphologically and semantically enhanced resource. In: SIGLEXGoogle Scholar
  10. 10.
    Vázquez, S., Montoyo, A., Rigau, G.: Using relevant domains resource for word sense disambiguation. In: IC-AI, pp. 784–789 (2004)Google Scholar

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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