Journal of Psycholinguistic Research

, Volume 30, Issue 4, pp 419–435 | Cite as

Verb Frame Frequency as a Predictor of Verb Bias

  • Maria Lapata
  • Frank Keller
  • Sabine Schulte im Walde
Article

Abstract

There is considerable evidence showing that the human sentence processor is guided by lexical preferences in resolving syntactic ambiguities. Several types of preferences have been identified, including morphological, syntactic, and semantic ones. However, the literature fails to provide a uniform account of what lexical preferences are and how they should be measured. The present paper provides evidence for the view that lexical preferences are records of prior linguistic experience. We show that a type of lexial syntactic preference, viz., verb biases as measured by norming experiments, can be approximated by verb frame frequencies extracted from a large, balanced corpus using computational learning techniques.

sentence processing verb bias lexical preferences verb frames chunking 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

REFERENCES

  1. Abney, S. (1997). Part-of-speech tagging and partial parsing. In S. Young & G. Bloothooft (Eds.), Corpus-based methods in language and speech (pp. 118-136). Dordrecht: Kluwer.Google Scholar
  2. Baum, L. E. (1972). An inequality and associated maximization technique in statistical estimation for probabilistic functions of Markov processes. Inequalities, 3, 1-8.Google Scholar
  3. Brent, M. (1993). From grammar to lexicon: Unsupervised learning of lexical syntax. Computational Linguistics, 19, 243-262.Google Scholar
  4. Briscoe, T., & Carroll, J. (1997). Automatic extraction of subcategorization from corpora. Proceedings of the 5th Conference on Applied Natural Language Processing (pp. 46-55). Washington, DC: Association for Computational Linguistics.Google Scholar
  5. Burnard, L. (1995). Users guide for the British National Corpus. British National Corpus Consortium, Oxford University Computing Service.Google Scholar
  6. Carroll, G., & Rooth, M. (1998). Valence induction with a head-lexicalized PCFG. Proceedings of the 3rd Conference on Empirical Methods in Natural Language Processing (pp. 36-45). Granada: Association for Computational Linguistics.Google Scholar
  7. Connine, C. M., Ferreira, F., Jones, C., Clifton, C., & Frazier, L. (1984). Verb frame preferences: Descriptive norms. Journal of Psycholinguistic Research, 13, 307-319.Google Scholar
  8. Corley, S., Corley, M., Keller, F., Crocker, M. W., & Trewin, S. (2001). Finding syntactic structure in unparsed corpora: The Gsearch corpus query system. Computers and the Humanities, 35, 81-94.Google Scholar
  9. Garnsey, S. M., Lotocky, M. A., Pearlmutter, N. J., & Myers, E. M. (1997). Argument structure frequency biases for 100 sentence-complement-taking verbs. Unpublished manuscript, University of Illinois at Urbana-Champaign.Google Scholar
  10. Garnsey, S. M., Pearlmutter, N. J., Myers, E. M., & Lotocky, M. A. (1997). The contributions of verb bias and plausibility to the comprehension of temporarily ambiguous sentences. Journal of Memory and Language, 37, 58-93.Google Scholar
  11. Gibson, E., & Schütze, C. T. (1999). Disambiguation preferences in noun phrase conjunction do not mirror corpus frequency. Journal of Memory and Language, 40, 263-279.Google Scholar
  12. Gibson, E., Schütze, C. T., & Salomon, A. (1996). The relationship between the frequency and the processing complexity of linguistic structure. Journal of Psycholinguistic Research, 25, 59-92.Google Scholar
  13. Manning, C. D. (1993). Automatic acquisition of a large subcategorization dictionary from corpora. In Proceedings of the 31st Annual Meeting of the Association for Computational Linguistics (pp. 235-242). Columbus, OH: Association for Computational Linguistics.Google Scholar
  14. Marcus, M. P., Santorini, B., & Marcinkiewicz, M. A. (1993). Building a large annotated corpus of English: The Penn Treebank. Computational Linguistics, 19, 313-330.Google Scholar
  15. Merlo, P. (1994). A corpus-based analysis of verb continuation frequencies for syntactic processing. Journal of Psycholinguistic Research, 23, 435-457.Google Scholar
  16. Mitchell, D. C., Cuetos, F., Corley, M. M. B., & Brysbaert, M. (1996). Exposure-based models of human parsing: Evidence for the use of coarse-grained (non-lexical) statistical records. Journal of Psycholinguistic Research, 24, 469-488.Google Scholar
  17. Pickering, M. J., Traxler, M. J., & Crocker, M. W. (2000). Ambiguity resolution in sentence processing: Evidence against frequency-based accounts. Journal of Memory and Language, 43, 447-475.Google Scholar
  18. Roland, D., & Jurafsky, D. (2001). Verb sense and verb subcategorization probabilities. In S. Stevenson & P. Merlo (Eds.), The lexical basis of sentence processing: Formal, computational, and experimental issues. (in press). Amsterdam: John Bejamins.Google Scholar
  19. Schulte im Walde, S. (2000). Clustering verbs semantically according to their alternation behaviour. Proceedings of the 18th International Conference on Computational Linguistics (pp. 747-753). Saarbrücken/Luxembourg/ Nancy.Google Scholar
  20. Sturt, P., Pickering, M. J., & Crocker, M. W. (1999). Structural change and reanalysis difficulty in language comprehension. Journal of Memory and Language, 40, 136-150.Google Scholar
  21. Trueswell, J. C. (1996). The role of lexical frequency in syntactic ambiguity resolution. Journal of Memory and Language, 35, 566-585.Google Scholar
  22. Trueswell, J. C., Tanenhaus, M. K., & Kello, C. (1993). Verb-specific constraints in sentence processing: Separating effects of lexical preference from garden-paths. Journal of Experimental Psychology: Learning, Memory, and Cognition, 19, 528-553.Google Scholar

Copyright information

© Plenum Publishing Corporation 2001

Authors and Affiliations

  • Maria Lapata
    • 1
  • Frank Keller
    • 1
  • Sabine Schulte im Walde
    • 2
  1. 1.Institute for Communicating and Collaborative Systems, Division of InformaticsUniversity of EdinburghEdinburghUK
  2. 2.Institute for Natural Language ProcessingUniversity of StuttgartStuttgartGermany

Personalised recommendations