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Unsupervised Acquiring of Morphological Paradigms from Tokenized Text

  • Daniel Zeman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5152)

Abstract

This paper describes a rather simplistic method of unsupervised morphological analysis of words in an unknown language. All what is needed is a raw text corpus in the given language. The algorithm looks at words, identifies repeatedly occurring stems and suffixes, and constructs probable morphological paradigms. The paper also describes how this method has been applied to solve the Morpho Challenge 2007 task, and gives the Morpho Challenge results. Although quite simple, this approach outperformed, to our surprise, several others in most morpheme segmentation subcompetitions. We believe that there is enough room for improvements that can put the results even higher. Errors are discussed in the paper; together with suggested adjustments in future research.

Keywords

Unknown Word Unsupervised Segmentation Unknown Language Grammatical Meaning Morphological Paradigm 
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.
    Déjean, H.: Morphemes as Necessary Concepts for Structures Discovery from Untagged Corpora. In: Worksh. on Paradigms and Grounding in Nat. Lang. Learning, pp. 295–299 (1998)Google Scholar
  2. 2.
    Keshava, S., Pitler, E.: A Simple, Intuitive Approach to Morpheme Induction. In: PASCAL Challenge Works. on Unsup. Segm. of Words into Morphemes, Southampton (2006)Google Scholar
  3. 3.
    Dasgupta, S., Ng, V.: High-Performance, Language-Independent Morphological Segmentation. In: Proc. of NAACL HLT, Rochester, pp. 155–163 (2007)Google Scholar
  4. 4.
    Creutz, M.: Unsupervised Segmentation of Words Using Prior Distributions of Morph Length and Frequency. In: Proc. of ACL, Sapporo (2003)Google Scholar
  5. 5.
    Koskenniemi, K.: Two-level Morphology: A General Computational Model for Word-form Recognition and Production. Pub. No. 11. U. of Helsinki, Dept. of Gen. Ling (1983)Google Scholar
  6. 6.
    Hajič, J.: Disambiguation of Rich Inflection (Computational Morphology of Czech). Univerzita Karlova, MFF, Ústav formální a aplikované lingvistiky. Praha (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Daniel Zeman
    • 1
  1. 1.Ústav formální a aplikované lingvistikyUniverzita KarlovaPrahaCzechia

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