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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)

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.

Keywords

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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References

  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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