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Clustering Polish Texts with Latent Semantic Analysis

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Artifical Intelligence and Soft Computing (ICAISC 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6114))

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Abstract

The document clustering is an important technique of Natural Language Processing (NLP). The paper presents performance of partitional and agglomerative algorithms applied to clustering large number of Polish newspaper articles. We investigate different representations of the documents. The focus of the paper is on the applicability of the Latent Semantic Analysis to such clustering for Polish.

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Author information

Authors and Affiliations

  1. Institute of Computer Science, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059, Kraków, Poland

    Marcin Kuta & Jacek Kitowski

Authors
  1. Marcin Kuta
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  2. Jacek Kitowski
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Editor information

Editors and Affiliations

  1. Department of Artificial Intelligence, Academy of Humanities and Economics, Poland

    Leszek Rutkowski

  2. Academy of Humanities and Economics in Łódź, ul. Rewolucji 1905 nr 64, Łódź, Poland

    Rafał Scherer

  3. Institute of Automatics, AGH University of Science and Technology, Al. Mickiewicza 30, PL-30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  4. Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley Initiative in Soft Computing (BISC), 94720-1776, CA

    Lotfi A. Zadeh

  5. Computational Intelligence Laboratory Department of Electrical and Computer Engineering, University of Louisville, 40292, Louisville, KY

    Jacek M. Zurada

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Kuta, M., Kitowski, J. (2010). Clustering Polish Texts with Latent Semantic Analysis. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artifical Intelligence and Soft Computing. ICAISC 2010. Lecture Notes in Computer Science(), vol 6114. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13232-2_65

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  • DOI: https://doi.org/10.1007/978-3-642-13232-2_65

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13231-5

  • Online ISBN: 978-3-642-13232-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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