fsm2 – A Scripting Language for Creating Weighted Finite-State Morphologies

  • Thomas Hanneforth
Part of the Communications in Computer and Information Science book series (CCIS, volume 41)

Abstract

The present article describes fsm2, a software program which can be used interactively or as a script interpreter to manipulate weighted finite-state automata with around 100 different commands. fsm2 is based on FSM<2.0> – an efficient C++ template library to create and algebraically manipulate weighted automata. fsm2 is particularly well suited to create morphological analysers on the basis of weighted automata.

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References

  1. 1.
    Beesley, K.R., Karttunen, L.: Finite State Morphology. CSLI, Stanford (2003)Google Scholar
  2. 2.
    Roark, B., Sproat, R.: Computational Approaches to Syntax and Morphology. Oxford University Press, Oxford (2007)Google Scholar
  3. 3.
    Kuich, W., Salomaa, A.: Semirings, Automata, Languages. EATCS Monographs on Theoretical Computer Science, vol. 5. Springer, Heidelberg (1986)CrossRefMATHGoogle Scholar
  4. 4.
    Mohri, M.: Semiring frameworks and algorithms for shortest-distance problems. Journal of Automata, Languages and Combinatorics 7(3), 321–350 (2002)MathSciNetMATHGoogle Scholar
  5. 5.
    Geyken, A., Hanneforth, T.: TAGH: A complete morphology for german based on weighted finite-state automata. In: Yli-Jyrä, A., Karttunen, L., Karhumäki, J. (eds.) FSMNLP 2005. LNCS (LNAI), vol. 4002, pp. 55–66. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    Jurafsky, D., Martin, J.H.: Speech and Language Processing. Prentice Hall Series in Artificial Intelligence. Prentice Hall, Upper Saddle River (2000)Google Scholar
  7. 7.
    Hanneforth, T.: Using ranked semirings for representing morphology automata. In: Mahlow, C., Piotrowski, M. (eds.) Proceedings of SFCM. Springer, Heidelberg (to appear) Google Scholar
  8. 8.
    Mohri, M.: Weighted automata algorithms. In: Droste, M., Kuich, W., Vogler, H. (eds.) Handbook of Weighted Automata. Springer, Heidelberg (2009)Google Scholar
  9. 9.
    Mohri, M.: Finite-state transducers in language and speech processing. Computational Linguistics 23(2), 269–311 (1997)MathSciNetGoogle Scholar
  10. 10.
    Schiller, A., Teufel, S., Stöckert, C., Thielen, C.: Guidelines für das Tagging deutscher Textcorpora mit STTS. Technical report, Institut fur maschinelle Sprachverarbeitung, Stuttgart (1999)Google Scholar
  11. 11.
    Schiller, A.: German compound analysis with fsc. In: Yli-Jyrä, A., Karttunen, L., Karhumäki, J. (eds.) FSMNLP 2005. LNCS (LNAI), vol. 4002, pp. 239–246. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  12. 12.
    Junczys-Dowmunt, M.: Influence of accurate compound noun splitting on bilingual vocabulary extraction. In: Storrer, A., Geyken, A., Siebert, A., Würzner, K.M. (eds.) Selected Papers from the 9th Conference on Natural Language Processing KONVENS 2008, Berlin, Mouton de Gruyter, pp. 91–104. Mouton de Gruyter, Berlin (2008)Google Scholar
  13. 13.
    Lindén, K., Pirinen, T.: Weighted finite-state morphological analysis of Finnish compounding with hfst-lexc. In: Jokinen, K., Bick, E. (eds.) NODALIDA 2009 Conference Proceedings, pp. 89–95 (2009)Google Scholar
  14. 14.
    Mohri, M., Pereira, F.C.N.: Dynamic compilation of weighted context-free grammars. In: Proceedings of ACL 1998, pp. 891–897 (1998)Google Scholar
  15. 15.
    Hopcroft, J.E., Ullman, J.D.: Introduction to Automata Theory, Languages and Computation. Addison-Wesley Series in Computer Science. Addison-Wesley Publishing Company, Reading (1979)MATHGoogle Scholar
  16. 16.
    Amtrup, J.W.: Efficient finite state unification morphology. In: COLING 2004: Proceedings of the 20th international conference on Computational Linguistics, Morristown, NJ, USA, vol. 453. Association for Computational Linguistics (2004)Google Scholar
  17. 17.
    Katz, S.M.: Estimation of probabilities from sparse data for the language model component of a speech recognizer. IEEE Transactions on Acoustics, Speech and Signal Processing 35(3), 400–401 (1987)CrossRefGoogle Scholar
  18. 18.
    Jelinek, F.: Statistical Methods for Speech Recognition. In: Language, Speech and Communication. MIT Press, Cambridge (1997)Google Scholar
  19. 19.
    Aho, A.V., Corasick, M.J.: Efficient string matching: An aid to bibliographic search. Communications of the Asscociation for Computing Machinery 18(6), 333–340 (1975)MathSciNetCrossRefMATHGoogle Scholar
  20. 20.
    Daciuk, J., Watson, B.W., Mihov, S., Watson, R.E.: Incremental construction of minimal acyclic finite-state automata. Computational Linguistics 26(1), 3–16 (2000)MathSciNetCrossRefMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Thomas Hanneforth
    • 1
  1. 1.Department for LinguisticsUniversity of PotsdamGermany

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