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Using Semantic Classes as Document Keywords

  • Rubén Izquierdo
  • Armando Suárez
  • German Rigau
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6716)

Abstract

Keyphrases are mainly words that capture the main topics of a document. We think that semantic classes can be used as keyphrases for a text. We have developed a semantic class–based WSD system that can tag the words of a text with their semantic class. A method is developed to compare the semantic classes of the words of a text with the correct ones based on statistical measures. We find that the evaluation of semantic classes considered as keyphrases is very close to 100% in most cases.

Keywords

Word Sense Disambiguation Computational Linguistics Support Vector Machine Algorithm Semantic Class Human Language Technology 
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.
    Izquierdo, R., Suarez, A., Rigau, G.: Exploring the automatic selection of basic level concepts.In: Angelova, G., et.al. (eds.) International Conference Recent Advances in Natural Language Processing, Borovets, Bulgaria, pp. 298–302 (2007)Google Scholar
  2. 2.
    Izquierdo, R., Suárez, A., Rigau, G.: An empirical study on class-based word sense disambiguation. In: Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics, Athens, Greece, EACL 2009, pp. 389–397. Association for Computational Linguistics, Stroudsburg (2009)Google Scholar
  3. 3.
    Jones, K.S.: A statistical interpretation of term specificity and its application in retrieval. Journal of Documentation 28, 11–21 (1972)CrossRefGoogle Scholar
  4. 4.
    Miller, G., Leacock, C., Tengi, R., Bunker, R.: A Semantic Concordance. In: Proceedings of the ARPA Workshop on Human Language Technology (1993)Google Scholar
  5. 5.
    Spearman, C.: The proof and measurement of association between two things. The American Journal of Psychology 15(1), 72–101 (1904)CrossRefGoogle Scholar
  6. 6.
    Turney, P.D.: Learning algorithms for keyphrase extraction. Inf. Retr. 2, 303–336 (2000), http://portal.acm.org/citation.cfm?id=593957.593993 CrossRefGoogle Scholar
  7. 7.
    Witten, I.H., Paynter, G., Frank, E., Gutwin, C., Nevill-Manning, C.G.: KEA: Practical Automatic Keyphrase Extraction. In: Proceedings of Digital Libraries 1999 (DL'99), pp. 254–255 (1999), http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.55.3127

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Rubén Izquierdo
    • 1
  • Armando Suárez
    • 1
  • German Rigau
    • 2
  1. 1.GPLSI GroupUniversity of AlicanteSpain
  2. 2.IXA NLP GroupEHUDonostiaSpain

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