Compressed Storage of Sparse Finite-State Transducers

  • George Anton Kiraz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2214)


This paper presents an eclectic approach for compressing weighted finite-state automata and transducers, with minimal impact on performance. The approach is eclectic in the sense that various complementary methods have been employed: row-indexed storage of sparse matrices, dictionary compression, bit manipulation, and lossless omission of data. The compression rate is over 83% with respect to the current Bell Labs finite-state library.


Destination State Compression Rate Sparse Matrice Input Symbol Output Symbol 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Halle, M. and S. Keyser. 1971. English Stress, Its Forms, Its Growth, and Its Role in Verse. Studies in Language. Harper & Row, New York.Google Scholar
  2. Kaplan, R. and M. Kay. 1994. Regularmo dels of phonological rule systems. Computational Linguistics, 20(3):331–78.Google Scholar
  3. Karttunen, L. and K. Beesley. 1992. Two-level rule compiler. Technical report, Palo Alto Research Center, Xerox Corporation.Google Scholar
  4. Kay, M. and R. Kaplan. 1983. Word recognition. This paper was never published. The core ideas are published in Kaplan and Kay (1994).Google Scholar
  5. Koskenniemi, K. 1983. Two-Level Morphology. Ph.D. thesis, University of Helsinki.Google Scholar
  6. Liang, F. 1983. Word Hy-phen-a-tion by Comp-uter. Ph.D. thesis, Stanford Univeristy.Google Scholar
  7. Mohri, M., F. Pereira, and M. Riley. 1998. A rational design for a weighted finitestate transducer library. In D. Wood and S. Yu, editors, Automata Implementation, Lecture Notes in Computer Science 1436. Springer, pages 144–58.CrossRefGoogle Scholar
  8. Mohri, M. and R. Sproat. 1996. An efficient compiler for weighted rewrite rules. In Proceedings of the 34th Annual Meeting of the Association for Computational Linguistics, pages 231–8.Google Scholar
  9. Ritchie, G., A. Black, G. Russell, and S. Pulman. 1992. Computational Morphology: Practical Mechanisms for the English Lexicon. MIT Press, Cambridge, MA.Google Scholar
  10. Roche, E. and Y. Schabes. 1995. Deterministic part-of-speech tagging with finite-state transducers. Computational Linguistics, 21(2):227–53.Google Scholar
  11. Roche, E. and Y. Schabes, editors. 1997. Finite-State Language Processing. MIT Press.Google Scholar
  12. Sproat, R., editor. 1997. Multilingual Text-to-Speech Synthesis: The Bell Labs Approach. Kluwer, Boston, MA.Google Scholar
  13. Tarjan, R. and A. Yao. 1979. Storing a sparse table. Communications of the ACM, 22(11):606–11.zbMATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • George Anton Kiraz
    • 1
  1. 1.Bell Labs — Lucent TechnologiesMurray Hill

Personalised recommendations