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To Split or Not, and If so, Where? Theoretical and Empirical Aspects of Unsupervised Morphological Segmentation

  • Amit KirschenbaumEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9041)

Abstract

The purpose of this paper is twofold: First, it offers an overview of challenges encountered by unsupervised, knowledge free methods when analysing language data (with focus on morphology). Second, it presents a system for unsupervised morphological segmentation comprising two complementary methods that can handle a broad range of morphological processes. The first method collects words which share distributional and form similarity and applies Multiple Sequence Alignment to derive segmentation of these words. The second method then analyses less frequent words utilizing the segmentation results of the first method. The challenges presented in the theoretical part are demonstrated exemplarily on the workings and output of the introduced unsupervised system and accompanied by suggestions how to address them in future works.

Keywords

Word Form Related Word Morphological Process Computational Linguistics Candidate Pattern 
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.
    Baayen, R.H., Piepenbrock, R., Gulikers, L.: The CELEX lexical database (release 2). CD-ROM (1995)Google Scholar
  2. 2.
    Baroni, M., Matiasek, J., Trost, H.: Unsupervised discovery of morphologically related words based on orthographic and semantic similarity. In: Proceedings of the ACL 2002 Workshop on Morphological and Phonological Learning, pp. 48–57. Association for Computational Linguistics (July 2002)Google Scholar
  3. 3.
    Bernhard, D.: Morphonet: Exploring the use of community structure for unsupervised morpheme analysis. In: Peters, C., Di Nunzio, G.M., Kurimo, M., Mandl, T., Mostefa, D., Peñas, A., Roda, G. (eds.) CLEF 2009. LNCS, vol. 6241, pp. 598–608. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  4. 4.
    Bordag, S.: Unsupervised and knowledge-free morpheme segmentation and analysis. In: Peters, C., Jijkoun, V., Mandl, T., Müller, H., Oard, D.W., Peñas, A., Petras, V., Santos, D. (eds.) CLEF 2007. LNCS, vol. 5152, pp. 881–891. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  5. 5.
    Creutz, M., Lagus, K.: Unsupervised Morpheme Segmentation and Morphology Induction from Text Corpora Using Morfessor 1.0. Tech. Rep. Report A81, Helsinki University of Technology (March 2005)Google Scholar
  6. 6.
    Creutz, M., Lindén, K.: Morpheme segmentation gold standards for finnish and english. Publications in Computer and Information Science, Report A 77 (2004)Google Scholar
  7. 7.
    De Pauw, G., Wagacha, P.W.: Bootstrapping morphological analysis of gıkuyu using unsupervised maximum entropy learning. In: Proceedings of the Eighth Annual Conference of the International Speech Communication Association (2007)Google Scholar
  8. 8.
    Demberg, V.: A language-independent unsupervised model for morphological segmentation. In: Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pp. 920–927. Association for Computational Linguistics (June 2007)Google Scholar
  9. 9.
    1Fisher, D., Riloff, E.: Applying statistical methods to small corpora: Benefitting from a limited domain. In: Probabilistic Approaches to Natural Language, a AAAI Fall Symposium. pp. 47–53, technical Report FS-92-04 (1992)Google Scholar
  10. 10.
    Freitag, D.: Morphology induction from term clusters. In: Proceedings of the Ninth Conference on Computational Natural Language Learning, CONLL 2005, pp. 128–135. Association for Computational Linguistics (2005)Google Scholar
  11. 11.
    Gelbukh, A.F., Alexandrov, M., Han, S.: Detecting Inflection Patterns in Natural Language By Minimization of Morphological Model. In: Sanfeliu, A., Martínez Trinidad, J.F., Carrasco Ochoa, J.A. (eds.) CIARP 2004. LNCS, vol. 3287, pp. 432–438. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  12. 12.
    Gelbukh, A.F., Sidorov, G., Lara-Reyes, D., Chanona-Hernández, L.: Division of spanish words into morphemes with a genetic algorithm. In: Kapetanios, E., Sugumaran, V., Spiliopoulou, M. (eds.) NLDB 2008. LNCS, vol. 5039, pp. 19–26. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  13. 13.
    Goldsmith, J.: Unsupervised learning of the morphology of a natural language. Computational Linguistics 27(2), 153–198 (2001)CrossRefMathSciNetGoogle Scholar
  14. 14.
    Goldsmith, J.: An algorithm for the unsupervised learning of morphology. Natural Language Engineering 12(04), 353–371 (2006)CrossRefGoogle Scholar
  15. 15.
    Hammarström, H., Borin, L.: Unsupervised Learning of Morphology. Computational Linguistics 37(2), 309–350 (2011)CrossRefGoogle Scholar
  16. 16.
    Harris, Z.S.: Distributional Structure. In: Fodor, J.A., Katz, J.J. (eds.) The Structure of Language: Readings in the Philosophy of Language, pp. 33–46. Prentice-Hall (1964)Google Scholar
  17. 17.
    Holland, R.C.G., Down, T.A., Pocock, M.R., Prlic, A., Huen, D., James, K., Foisy, S., Dräger, A., Yates, A., Heuer, M., Schreiber, M.J.: BioJava: an open-source framework for bioinformatics. Bioinformatics 24(18), 2096–2097 (2008)CrossRefGoogle Scholar
  18. 18.
    Kazakov, D.: Unsupervised Learning of Naïve Morphology with Genetic Algorithms. In: Daelemans, W., van den Bosch, A., Weijters, A. (eds.) Workshop Notes of the ECML/MLnet Workshop on Empirical Learning of Natural Language Processing Tasks, pp. 105–112 (1997)Google Scholar
  19. 19.
    Kirschenbaum, A.: Unsupervised segmentation for different types of morphological processes using multiple sequence alignment. In: Dediu, A.-H., Martín-Vide, C., Mitkov, R., Truthe, B. (eds.) SLSP 2013. LNCS, vol. 7978, pp. 152–163. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  20. 20.
    Kurimo, M., Creutz, M., Varjokallio, M., Arisoy, E., Saraclar, M.: Unsupervised segmentation of words into morphemes-challenge 2005: An introduction and evaluation report. In: Proceedings of the PASCAL Challenge Workshop on Unsupervised Segmentation of Words into Morphemes (2006)Google Scholar
  21. 21.
    Monson, C., Hollingshead, K., Roark, B.: Probabilistic ParaMor. In: Working Notes for the CLEF 2009 Workshop (2009)Google Scholar
  22. 22.
    Needleman, S.B., Wunsch, C.D.: A general method applicable to the search for similarities in the amino acid sequence of two proteins. Journal of Molecular Biology 48(3), 443–453 (1970)CrossRefGoogle Scholar
  23. 23.
    Notredame, C.: Recent progresses in multiple sequence alignment: a survey. Pharmacogenomics 3(1) (2002)Google Scholar
  24. 24.
    Rodrigues, P., Ćavar, D.: Learning arabic morphology using statistical constraint-satisfaction models. In: Benmamoun, E. (ed.) Perspectives on Arabic Linguistics XIX, pp. 63–75. John Benjamins (2007)Google Scholar
  25. 25.
    Schone, P., Jurafsky, D.: Knowledge-Free Induction of Morphology Using Latent Semantic Analysis. In: Proceedings of the 4th Conference on Computational Natural Language Learning, vol. 7, pp. 67–72. Association for Computational Linguistics (2000)Google Scholar
  26. 26.
    Schone, P., Jurafsky, D.: Knowledge-free induction of inflectional morphologies. In: Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language Technologies, NAACL 2001, Association for Computational Linguistics (2001)Google Scholar
  27. 27.
    Tchoukalov, T., Monson, C., Roark, B.: Morphological Analysis by Multiple Sequence Alignment. In: Peters, C., Di Nunzio, G.M., Kurimo, M., Mandl, T., Mostefa, D., Peñas, A., Roda, G. (eds.) CLEF 2009. LNCS, vol. 6241, pp. 666–673. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  28. 28.
    Virpioja, S., Turunen, V.T., Spiegler, S., Kohonen, O., Kurimo, M.: Empirical comparison of evaluation methods for unsupervised learning of morphology. Traitement Automatique des Langues 52(2), 45–90 (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Natural Language Processing GroupLeipzig UniversityLeipzigGermany

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