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)


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.


Regular Expression Input String Grammar Rule Replacement Rule Path Property 
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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  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)CrossRefzbMATHGoogle Scholar
  4. 4.
    Mohri, M.: Semiring frameworks and algorithms for shortest-distance problems. Journal of Automata, Languages and Combinatorics 7(3), 321–350 (2002)MathSciNetzbMATHGoogle 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)zbMATHGoogle 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)MathSciNetCrossRefzbMATHGoogle 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)MathSciNetCrossRefzbMATHGoogle Scholar

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© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

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

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