Parsing and Hypergraphs

  • Dan Klein
  • Christopher D. Manning
Part of the Text, Speech and Language Technology book series (TLTB, volume 23)

Abstract

While symbolic parsers can be viewed as deduction systems, this view is less natural for probabilistic parsers. We present a view of parsing as directed hypergraph analysis, which naturally covers both symbolic and probabilistic parsing. We illustrate the approach by showing how a dynamic extension of Dijkstra’s algorithm can be used to construct a probabilistic chart parser with an O(n3) time bound for arbitrary PCFGs, while preserving as much of the flexibility of symbolic chart parsers as is allowed by the inherent ordering of probabilistic dependencies.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Baker, J. K. (1979). Trainable grammars for speech recognition. In D. H. Klatt and J. J. Wolf (Eds.), Speech Communication Papers for the 97th Meeting of the Acoustical Society of America, 547–550.Google Scholar
  2. Bistarelli, S., U. Montanari, and F. Rossi (1997). Semiring-based constraint satisfaction and optimization. Journal of the ACM 44(2):201–236.MathSciNetMATHCrossRefGoogle Scholar
  3. Caraballo, S. A., and E. Charniak (1998). New figures of merit for best-first probabilistic chart parsing. Computational Linguistics 24:275–298.Google Scholar
  4. Chappelier, J.-C., and M. Rajman (1998). A generalized CYK algorithm for parsing stochastic CFG. In First Workshop on Tabulation in Parsing and Deduction (TAPD98), 133–137, Paris.Google Scholar
  5. Earley, J. (1970). An efficient context-free parsing algorithm. Communications of the ACM 6(8):451–455.Google Scholar
  6. Gallo, G., G. Longo, S. Pallottino, and S. Nguyen. (1993). Directed hypergraphs and applications. Discrete Applied Mathematics 42:177–201.MathSciNetMATHCrossRefGoogle Scholar
  7. Gazdar, G., and C. Mellish (1989). Natural Language Processing in Prolog. Wokingham, England: Addison-Wesley.Google Scholar
  8. Goodman, J. (1998). Parsing inside-out. PhD thesis, Harvard University.Google Scholar
  9. Graham, S. L., M. A. Harrison, and W. L. Ruzzo. (1980). An improved context-free recognizer. ACM Transactions on Programming Languages and Systems 2(3):415–462.MATHCrossRefGoogle Scholar
  10. Jelinek, F, J. D. Lafferty, and R. L. Mercer (1992). Basic methods of probabilistic context free grammars. In P. Laface and R. De Mori (Eds.), Speech Recognition and Understanding: Recent Advances, Trends, and Applications, Vol. 75 of Series F: Computer and Systems Sciences. Springer Verlag.Google Scholar
  11. Kasami, T. (1965). An efficient recognition and syntax analysis algorithm for context-free languages. Technical Report AFCRL-65-758, Air Force Cambridge Research Laboratory, Bedford, MA.Google Scholar
  12. Kay, M. (1980). Algorithm schemata and data structures in syntactic processing. Technical Report CSL-80-12, Xerox PARC, Palo Alto, CA.Google Scholar
  13. Klein, D., and C. D. Manning (2001 a). An O(n 3) agenda-based chart parser for arbitrary probabilistic context-free grammars. Technical Report dbpubs/2001-16, Stanford University, Stanford, CA.Google Scholar
  14. Klein, D., and C. D. Manning (2001 b). Parsing with treebank grammars: Empirical bounds, theoretical models, and the structure of the Penn treebank. In ACL 39/EACL 10, 330–337.Google Scholar
  15. Klein, D., and C. D. Manning (2002). A* parsing: Fast exact Viterbi parse selection. Technical Report dbpubs/2002-16, Stanford University.Google Scholar
  16. Knuth, D. E. (1977). A generalization of Dijkstra’s algorithm. Information Processing Letters 6(1):1–5.MathSciNetMATHCrossRefGoogle Scholar
  17. Kupiec, J. (1991). A trellis-based algorithm for estimating the parameters of a hidden stochastic context-free grammar. In Proceedings of the Speech and Natural Language Workshop, 241–246. DARPA.Google Scholar
  18. Mohri, M. (1997). Finite-state transducers in language and speech processing. Computational Linguistics 23(4):269–311.MathSciNetGoogle Scholar
  19. Moore, R. C. (2000). Improved left-corner chart parsing for large context-free grammars. In Proceedings of the Sixth International Workshop on Parsing Technologies.Google Scholar
  20. Pereira, F., and S. M. Shieber (1987). Prolog and Natural-Language Analysis. Stanford, CA: CSLI Publications.MATHGoogle Scholar
  21. Pereira, F. C., and D. H. Warren (1983). Parsing as deduction. In ACL 21, 137–144.Google Scholar
  22. Shieber, S., Y. Schabes, and F. Pereira (1995). Principles and implementation of deductive parsing. Journal of Logic Programming 24:3–36.MathSciNetMATHCrossRefGoogle Scholar
  23. Sikkel, K., and A. Nijholt (1997). Parsing of Context-Free languages. In G. Rozenberg and A. Salomaa (Eds.), Handbook of Formal Languages, Vol. 2: Linear Modelling: Background and Application, 61–100. Berlin: Springer.CrossRefGoogle Scholar
  24. Stolcke, A. (1995). An efficient probabilistic context-free parsing algorithm that computes prefix probabilities. Computational Linguistics 21:165–202.MathSciNetGoogle Scholar
  25. Younger, D. H. (1967). Recognition and parsing of context free languages in time n 3. Information and Control 10:189–208.MATHCrossRefGoogle Scholar

Copyright information

© Kluwer Academic Publishers 2004

Authors and Affiliations

  • Dan Klein
    • 1
  • Christopher D. Manning
    • 2
  1. 1.Department of Computer ScienceStanford UniversityStanfordUSA
  2. 2.Departments of Computer Science and LinguisticsStanford UniversityStanfordUSA

Personalised recommendations