Improving the Clustering of Blogosphere with a Self-term Enriching Technique

  • Fernando Perez-Tellez
  • David Pinto
  • John Cardiff
  • Paolo Rosso
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5729)

Abstract

The analysis of blogs is emerging as an exciting new area in the text processing field which attempts to harness and exploit the vast quantity of information being published by individuals. However, their particular characteristics (shortness, vocabulary size and nature, etc.) make it difficult to achieve good results using automated clustering techniques. Moreover, the fact that many blogs may be considered to be narrow domain means that exploiting external linguistic resources can have limited value. In this paper, we present a methodology to improve the performance of clustering techniques on blogs, which does not rely on external resources. Our results show that this technique can produce significant improvements in the quality of clusters produced.

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References

  1. 1.
    Banerjee, S., Pedersen, T.: An adapted lesk algorithm for word sense disambiguation using wordNet. In: Gelbukh, A. (ed.) CICLing 2002. LNCS, vol. 2276, pp. 136–145. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  2. 2.
    Bojars, U., Breslin, J.G., Passant, A.: SIOC Browser Towards a richer blog browsing experience. In: The 4th BlogTalk Conference (2006)Google Scholar
  3. 3.
    Daille, B.: Qualitative terminology extraction. In: Bourigault, D., Jacquemin, C., et l’homme, M.-C. (eds.) Recent Advances in Computational Terminology. Natural Language Processing, vol. 2, pp. 149–166. John Benjamins (2001)Google Scholar
  4. 4.
    Fellbaum, C.: WordNet: An Electronic Lexical Database. MIT Press, Cambridge (1998)Google Scholar
  5. 5.
    Grefenstette, G.: Explorations in Automatic Thesaurus Discovery. Kluwer Academic Publishers, Dordrecht (1994)CrossRefGoogle Scholar
  6. 6.
    MacQueen, J.B.: Some methods for classification and analysis of multivariate observations. In: Proc. of the 5th Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297. University of California Press, Berkeley (1967)Google Scholar
  7. 7.
    Manning, D.C., Schütze, H.: Foundations of statistical natural language processing. MIT Press, Cambridge (1999)Google Scholar
  8. 8.
    Nakagawa, H., Mori, T.: A Simple but Powerful Automatic Term Extraction Method, International Conference on Computational Linguistics. In: COLING 2002 on COMPUTERM 2002: second international workshop on computational terminology, vol. 14 (2002)Google Scholar
  9. 9.
    Perez-Tellez, F., Pinto, D., Rosso, P., Cardiff, J.: Characterizing Weblog Corpora. In: 14th International Conference on Applications of Natural Language to Information Systems (2009)Google Scholar
  10. 10.
    Pinto, D., Rosso, P., Jiménez-Salazar, H.: UPV-SI: Word Sense Induction using Self-Term Expansion. In: 4th Workshop on Semantic Evaluations - SemEval 2007, Association for Computational Linguistics (2007)Google Scholar
  11. 11.
    Pinto, D.: On Clustering and Evaluation of Narrow Domain Short-Text Corpora, PhD dissertation, Universidad Politécnica de Valencia, Spain (2008)Google Scholar
  12. 12.
    Qiu, Y., Frei, H.P.: Concept based query expansion. In: Proc. of the 16th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 160–169. ACM Press, New York (1993)Google Scholar
  13. 13.
    Shin, K., Han, S.Y.: Fast clustering algorithm for information organization. In: Gelbukh, A. (ed.) CICLing 2003. LNCS, vol. 2588, pp. 619–622. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  14. 14.
    Spärck, J.K.: A statistical interpretation of term specificity and its application in retrieval. Journal of Documentation 28(1), 11–21 (1972)CrossRefGoogle Scholar
  15. 15.
    Van Rijsbergen, C.J.: Information Retireval. Butterworths, London (1979)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Fernando Perez-Tellez
    • 1
  • David Pinto
    • 2
  • John Cardiff
    • 1
  • Paolo Rosso
    • 3
  1. 1.Institute of Technology TallaghtSocial Media Research GroupDublinIreland
  2. 2.Benemerita Universidad Autónoma de PueblaMexico
  3. 3.Natural Language Engineering Lab. - EliRF, Dept. Sistemas Informáticos y ComputaciónUniversidad PolitécnicaValenciaSpain

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