Detecting Inflection Patterns in Natural Language by Minimization of Morphological Model

  • Alexander Gelbukh
  • Mikhail Alexandrov
  • Sang-Yong Han
Conference paper

DOI: 10.1007/978-3-540-30463-0_54

Part of the Lecture Notes in Computer Science book series (LNCS, volume 3287)
Cite this paper as:
Gelbukh A., Alexandrov M., Han SY. (2004) Detecting Inflection Patterns in Natural Language by Minimization of Morphological Model. In: Sanfeliu A., Martínez Trinidad J.F., Carrasco Ochoa J.A. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2004. Lecture Notes in Computer Science, vol 3287. Springer, Berlin, Heidelberg

Abstract

One of the most important steps in text processing and information retrieval is stemming – reducing of words to stems expressing their base meaning, e.g., bake, baked, bakes, bakingbak-. We suggest an unsupervised method of recognition such inflection patterns automatically, with no a priori information on the given language, basing exclusively on a list of words extracted from a large text. For a given word list V we construct two sets of strings: stems S and endings E, such that each word from V is a concatenation of a stem from S and ending from E. To select an optimal model, we minimize the total number of elements in S and E. Though such a simplistic model does not reflect many phenomena of real natural language morphology, it shows surprisingly promising results on different European languages. In addition to practical value, we believe that this can also shed light on the nature of human language.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Alexander Gelbukh
    • 1
    • 2
  • Mikhail Alexandrov
    • 1
  • Sang-Yong Han
    • 2
  1. 1.National Polytechnic InstituteMexico
  2. 2.Chung-Ang UniversityKorea

Personalised recommendations