Elamur Research⌕
☰
home / papers / 2003 / A Probabilistic Account of Logical Metonymy

A Probabilistic Account of Logical Metonymy

Summary

Logical metonymy covers phrases where a verb or adjective needs an event, but the syntax supplies only an entity noun. For example, "enjoy the book" is understood as enjoying reading it, and "easy problem" as a problem that is easy to solve. Theoretical treatments, especially Pustejovsky's generative lexicon with its qualia roles, have two gaps. They do not list every available reading, and they do not say which readings are more likely. Purely pragmatic inference also overgenerates odd cases such as "?John began the bridge".

Lapata and Lascarides use a big body of text to learn how metonymic adjectives and verbs are interpreted. They exploit stable links between surface syntactic patterns and meaning to collect candidate paraphrasing verbs automatically. A probabilistic model then ranks these candidates. The rankings were compared with paraphrase judgments elicited from human participants, and the model's ordering correlates reliably with human intuitions.

The work turns a mostly theoretical phenomenon into an empirical, corpus-driven account of implicit events. It also shows that interpretation likelihood can be estimated rather than stipulated.

Key points

  • Scope. The paper covers two construction types:

    • Verb-noun cases, such as finish/begin/enjoy + object.
    • Adjective-noun cases, such as easy, difficult, good, fast.

    Adjectives differ in the role the noun plays for the implied verb. Fast favours a subject reading, easy/difficult favour an object reading, and good allows both (good cook vs good soup).

  • Relation to Pustejovsky's qualia structure. The paper contrasts its approach with qualia structure, which has a telic role (book → read) and an agentive role (book → write). It argues that a single default telic role is inadequate. Nouns differ in how ambiguous they are (fast scientist vs fast plane), and plausible interpretations need a likelihood ranking.

  • Prior empirical work. Earlier studies only measured how common metonymy is. Briscoe et al. (1990) used the LOB corpus (1M words), and Verspoor (1997) used the BNC (100M words). Neither generated interpretations automatically or ranked them.

  • Evaluation. Model rankings were tested against experimentally collected human paraphrase judgments, with reliable correlation.

    • The supplied excerpt omits the central sections, so exact correlation values, corpus statistics and model details are not reported here.
    • The reference list points to magnitude estimation, web-based experiments and WordNet, but their exact use is unconfirmed.

  • Publication. Computational Linguistics, 2003; authors from the University of Sheffield (Lapata) and the University of Edinburgh (Lascarides).

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Natural language processing

Institutions

🎓University of EdinburghEdinburgh · United Kingdom202 works · 55,753 citations🎓University of SheffieldSheffield · United Kingdom67 works · 17,924 citations

Cite

Cite as Maria Lapata and Alex Lascarides. 2003. A Probabilistic Account of Logical Metonymy. Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1162/089120103322145324

@article{lapata2003probabilistic,
  title = {{A Probabilistic Account of Logical Metonymy}},
  author = {Maria Lapata and Alex Lascarides},
  journal = {Computational Linguistics},
  year = {2003},
  doi = {10.1162/089120103322145324},
  url = {https://doi.org/10.1162/089120103322145324}
}

References

53 references, 4 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Abney, Steve. 1996. Partial parsing via finite-state cascades. In John Carroll, editor, Workshop on Robust Parsing, Prague. European Summer School in Logic, Language and Information, pages 8–15. find
  2. Apresjan, J. D. 1973. Regular polysemy. Linguistics, 142:5–32. find
  3. Bard, Ellen Gurman, Dan Robertson, and Antonella Sorace. 1996. Magnitude Max −24.62 −26.32 −27.92 find
  4. −15.11 −20.90 −22.22 −0.2697 −0.4361 −0.7269 0.3297 0.2324 0.0336 −23.99 −25.24 −25.85 −15.93 −20.24 −22.46 −0.68 −0.70 −0.68 0.49 0.31 0.37 estimation of linguistic acceptability. Language, 72(1):32–68. find
  5. Barzilay, Regina and Kathy McKeown. 2001. Extracting paraphrases from a parallel corpus. In Proceedings of the 39th Annual Meeting of the Association for Computational Linguistics, Toulouse, France, pages 50–57. our card
  6. Bouaud, Jacques, Bruno Bachimont, and Pierre Zweigenbaum. 1996. Processing metonymy: A domain-model heuristic graph traversal approach. In Proceedings of the 16th International Conference on Computational Linguistics, Copenhagen, Denmark, pages 137–142. find
  7. Briscoe, Ted, Ann Copestake, and Bran Boguraev. 1990. Enjoy the paper: Lexical semantics via lexicology. In Proceedings of the 13th International Conference on Computational Linguistics, Helsinki, Finland, pages 42–47. find
  8. Burnage, Gavin. 1990. Celex–A guide for users. Technical report, Centre for Lexical Information, University of Nijmegen, Nijmegen, the Netherlands. find
  9. Burnard, Lou. 1995. The Users Reference Guide for the British National Corpus. British National Corpus Consortium, Oxford University Computing Service, Oxford, U.K. find
  10. Clark, Stephen and David Weir. 2001. Class-based probability estimation using a semantic hierarchy. In Proceedings of the Second Conference of the North American Chapter of the Association for Computational Linguistics, Pittsburgh, Pennsylvania. find
  11. Collins, Michael and James Brooks. 1995. Prepositional phrase attachment through a backed-off model. In David Yarowsky and Kenneth W. Church, editors, Proceedings of the Third Workshop on Very Large Corpora, Cambridge, Massachusetts, pages 27–38. our card
  12. Copestake, A. 2001. The semi-generative lexicon: Limits on lexical productivity. In Proceedings of the First International Workshop on Generative Approaches to the Lexicon, Geneva. find
  13. Copestake, A. and E. J. Briscoe. 1995. Semi-productive polysemy and sense extension. Journal of Semantics, 12(1):15–67. find
  14. Copestake,A. and A. Lascarides. 1997. Integrating symbolic and statistical representations: The lexicon pragmatics interface. In Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and the Eighth Meeting of the European Chapter for the Association for Computationa… find
  15. Corley, Martin and Christoph Scheepers. 2002. Syntactic priming in English: Evidence from response latencies. Psychonomic Bulletin and Review, 9(1):126–131. find
  16. Cowart, Wayne. 1997. Experimental Syntax: Applying Objective Methods to Sentence Judgments. Sage, Thousand Oaks, California. find
  17. Daelemans, Walter, Antal van den Bosch, and Jakub Zavrel. 1999. Forgetting exceptions is harmful in language learning. Machine Learning, 34(1–3):11–43. find
  18. Fass, Dan. 1991. met*: A method for discriminating metonymy and metaphor by computer. Computational Linguistics, 17(1):49–90. our card
  19. Godard, Danièle and Jacques Jayez. 1993. Towards a proper treatment of coercion phenomena. In Proceedings of the 31st Annual Meeting of the Association for Computational Linguistics, Columbus, Ohio, pages 168–177. find
  20. Hobbs, Jerry R., Martin Stickel, Douglas Appelt, and Paul Martin. 1993. Interpretation as abduction. Artificial Intelligence, 63(1–2):69–142. find
  21. Inverson, Eric and Stephen Helmreich. 1992. An integrated approach to non-literal phrase interpretation. Computational Intelligence, 8(3):477–493. find
  22. Keller, Frank and Theodora Alexopoulou. 2001. Phonology competes with syntax: Experimental evidence for the interaction of word order and accent placement in the realization of information structure. Cognition, 79(3):301–371. find
  23. Keller, Frank and Ash Asudeh. 2002. Probabilistic learning algorithms and optimality theory. Linguistic Inquiry, 33(2):225–244. find
  24. Keller, Frank, Martin Corley, and Christoph Scheepers. 2001. Conducting psycholinguistic experiments over the World Wide Web. Unpublished manuscript, University of Edinburgh, Edinburgh, U.K., and Saarland University, Saarbruecken, Germany. find
  25. Lakoff, George and Mark Johnson. 1980. Metaphors We Live By. University of Chicago Press, Chicago. find
  26. Lapata, Maria, Frank Keller, and Scott McDonald. 2001. Evaluating smoothing algorithms against plausibility judgments. In Proceedings of the 39th Annual Meeting of the Association for Computational Linguistics, Toulouse, France, pages 346–353. find
  27. Lascarides, Alex and Ann Copestake. 1998. Pragmatics and word meaning. Journal of Linguistics, 34(2):387–414. find
  28. Leech, Geoffrey, Roger Garside, and Michael Bryant. 1994. The tagging of the British national corpus. In Proceedings of the 15th International Conference on Computational Linguistics, Kyoto, Japan, pages 622–628. find
  29. Levin, Beth. 1993. English Verb Classes and Alternations: A Preliminary Investigation. University of Chicago Press, Chicago. find
  30. Li, Hang and Naoki Abe. 1998. Generalizing case frames using a thesaurus and the MDL principle. Computational Linguistics, 24(2):217–244. our card
  31. Light, Marc. 1996. Morphological Cues for Lexical Semantics. Ph.D. thesis, University of Rochester. find
  32. Lodge, Milton. 1981. Magnitude Scaling: Quantitative Measurement of Opinions. Sage, Beverly Hills, California. find
  33. Macleod, Catherine, Ralph Grishman, Adam Meyers, Leslie Barrett, and Ruth Reeves. 1998. Comlex: A lexicon of nominalizations. In Proceedings of the Eighth International Congress of the European Association for Lexicography, Liège, Belgium, pages 187–193. find
  34. Markert, Katja and Udo Hahn. 1997. On the interaction of metonymies and anaphora. In Proceedings of the 15th International Joint Conference on Artificial Intelligence, Nagoya, Japan, pages 1010–1015. find
  35. McCarthy, Diana. 2000. Using semantic preferences to identify verbal participation in role switching alternations. In Proceedings of the First Conference of the North American Chapter of the Association for Computational Linguistics, Seattle, Washington, pages 256–263. find
  36. McElree, Brian, Matthew J. Traxler, Martin J. Pickering, Rachel E. Seely, and Ray Jackendoff. 2001. Reading time evidence for enriched composition. Cognition, (78)1:17–25. find
  37. Merlo, Paola and Susanne Stevenson. 2001. Automatic verb classification based on statistical distribution of argument structure. Computational Linguistics, 27(3):373–408. find
  38. Miller, George A., Richard Beckwith, Christiane Fellbaum, Derek Gross, and Katherine J. Miller. 1990. Introduction to WordNet: An on-line lexical database. International Journal of Lexicography, 3(4):235–244. find
  39. Nunberg, Geoffrey. 1995. Transfers of meaning. Journal of Semantics, 12(1):109–132. find
  40. Pustejovsky, James. 1991. The generative lexicon. Computational Linguistics, 17(4):409–441. find
  41. Pustejovsky, James. 1995. The Generative Lexicon. MIT Press, Cambridge. find
  42. Pustejovsky, James and Pierrette Bouillon. 1995. Logical polysemy and aspectual coercion. Journal of Semantics, 12:133–162. find
  43. Resnik, Philip. 1993. Selection and Information: A Class-Based Approach to Lexical Relationships. Ph.D. thesis, University of Pennsylvania, Philadelphia. find
  44. Resnik, Philip. 1999. Semantic similarity in a taxonomy: An information-based measure and its application to problems of ambiguity in natural language. Journal of Artificial Intelligence Research, 95–130. find
  45. Resnik, Philip and Mona Diab. 2000. Measuring verb similarity. In Lila R. Gleitman and Aravid K. Joshi, editors, Proceedings of the 22nd Annual Conference of the Cognitive Science Society. Erlbaum, Mahwah, New Jersey, pages 399–404. find
  46. Rooth, Mats, Stefan Riezler, Detlef Prescher, Glenn Carroll, and Franz Beil. 1999. Inducing a semantically annotated lexicon via em-based clustering. In Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics, College Park, Maryland, pages 104–111. find
  47. Schütze, Carson T. 1996. The Empirical Base of Linguistics: Grammaticality Judgments and Linguistic Methodology. University of Chicago Press, Chicago. find
  48. Siegel, Eric and Kathleen McKeown. 2000. Learning methods to combine linguistic indicators: Improving aspectual classification and revealing linguistic insights. Computational Linguistics, 26(4):595–629. find
  49. Stevens, S. Smith. 1975. Psychophysics: Introduction to Its Perceptual, Neural, and Social Prospects. Wiley, New York. find
  50. Utiyama, Masao, Masaki Murata, and Hitoshi Isahara. 2000. A statistical approach to the processing of metonymy. In Proceedings of the 18th International Conference on Computational Linguistics, Saarbrücken, Germany, pages 885–891. find
  51. Vendler, Zeno. 1968. Adjectives and Nominalizations. Mouton, The Hague, the Netherlands. find
  52. Verspoor, Cornelia Maria. 1997. Contextually-Dependent Lexical Semantics. Ph.D. thesis, University of Edinburgh, Edinburgh, U.K. find
  53. Weiss, Sholom M. and Casimir A. Kulikowski. 1991. Computer Systems that Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning, and Expert Systems. Morgan Kaufmann, San Mateo, California. find

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/papers/lapata-2003-probabilistic-account-logical-metonymy/