Clustering Narrow-Domain Short Texts Using K-Means, Linguistic Patterns and LSI

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 436)

Abstract

In the present work we consider the problem of narrow-domain clustering of short texts, such as academic abstracts. Our main objective is to check whether it is possible to improve the quality of k-means algorithm expanding the feature space by adding a dictionary of word groups that were selected from texts on the basis of a fixed set of patterns. Also, we check the possibility to increase the quality of clustering by mapping the feature spaces to a semantic space with a lower dimensionality using Latent Semantic Indexing (LSI). The results allow us to assume that the aforementioned modifications are feasible in practical terms as compared to the use of k-means in the feature space defined only by the main dictionary of the corpus.

Keywords

Clustering Short texts Narrow domain texts LSI Linguistic patterns 

References

  1. 1.
    Bernardini, A., Carpineto, C.: Full-subtopic retrieval with keyphrase-based search results clustering. In: IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technologies, vol. 1 (2009)Google Scholar
  2. 2.
    Zhang, D., Dong, Y.: Semantic, hierarchical, online clustering of Web search results. In: Yu, J.X., Lin, X., Lu, H., Zhang, Y. (eds.) APWeb 2004. LNCS, vol. 3007, pp. 69–78. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  3. 3.
    Zeng, HJ., He, QC., Chen, Zh., Ma, WY., Ma, J.: Learning to cluster web search results. In: Proceeding SIGIR ’04 Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 210–217 (2004)Google Scholar
  4. 4.
    Popova, S., Khodyrev, I., Egorov, A., Logvin, S., Gulyaev, S., Karpova, M., Mouromtsev, D.: Sci-search: academic search and analysis system based on keyphrases. In: Klinov, P., Mouromtsev, D. (eds.) KESW 2013. CCIS, vol. 394, pp. 281–288. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  5. 5.
    Alexandrov, M., Gelbukh, A., Rosso, P.: An approach to clustering abstracts. In: Montoyo, A., Muńoz, R., Métais, E. (eds.) NLDB 2005. LNCS, vol. 3513, pp. 275–285. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  6. 6.
    Cagnina, L., Errecalde, M., Ingaramo, D., Rosso, P.: A discrete particle swarm optimizer for clustering short text corpora. In: BIOMA08, p. 93103 (2008)Google Scholar
  7. 7.
    Errecalde, M., Ingaramo, D., Rosso, P.: ITSA: an effective iterative method for short-text clustering tasks. In: García-Pedrajas, N., Herrera, F., Fyfe, C., Benítez, J.M., Ali, M. (eds.) IEA/AIE 2010. LNCS, vol. 6096, pp. 550–559. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  8. 8.
    Pinto, D.: Analysis of narrow-domain short texts clustering. In: Research report for Diploma de Estudios Avanzados (DEA), Department of Information Systems and Computation, UPV (2007)Google Scholar
  9. 9.
    Pinto, D., Rosso, P., Jiménez, H.: A self-enriching methodology for clustering narrow domain short texts. Comput. J. 54(7), 1148–1165 (2011)CrossRefGoogle Scholar
  10. 10.
    Pinto, D., Jiménez-Salazar, H., Rosso, P.: Clustering abstracts of scientific texts using the transition point technique. In: Gelbukh, A. (ed.) CICLing 2006. LNCS, vol. 3878, pp. 536–546. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  11. 11.
    Hasanzadeh, E., Poyan, M., Rokny, H.: Text clustering on latent semantic indexing with particle swarm optimization (PSO) algorithm. Int. J. Phys. Sci. 7(1), 116–120 (2012)Google Scholar
  12. 12.
    Manning, C., Raghavan, P., Schutze, H.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2009)Google Scholar
  13. 13.
    Kim, S.N., Medelyan, O., Kan, M.Y., Baldwin, T.: Automatic keyphrase extraction from scientific articles. Lang. Resour. Eval. 47(3), 723–742 (2012)CrossRefGoogle Scholar
  14. 14.
    Eissen, S.M., Stein, B.: Analysis of clustering algorithms for Web-based search. In: Karagiannis, D., Reimer, U. (eds.) PAKM 2002. LNCS (LNAI), vol. 2569, pp. 168–178. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  15. 15.
    Stein, B., Meyer zu Eissen, S., Wißbrock, F.: On cluster validity and the information need of users. In: Hanza, MH. (ed.) 3rd IASTED International Conference on Artificial Intelligence and Applications (AIA 03), Benalmádena, Spain, pp. 216–221, ISBN 0-88986-390-3. ACTA Press, IASTED (2003)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Saint-Petersburg State UniversitySaint PetersburgRussia
  2. 2.ITMO UniversitySaint-PetersburgRussia
  3. 3.Autonomous University of BarcelonaBarcelonaSpain

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