Data and models for metonymy resolution
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We describe the first shared task for figurative language resolution, which was organised within SemEval-2007 and focused on metonymy. The paper motivates the linguistic principles of data sampling and annotation and shows the task’s feasibility via human agreement. The five participating systems mainly used supervised approaches exploiting a variety of features, of which grammatical relations proved to be the most useful. We compare the systems’ performance to automatic baselines as well as to a manually simulated approach based on selectional restriction violations, showing some limitations of this more traditional approach to metonymy recognition. The main problem supervised systems encountered is data sparseness, since metonymies in general tend to occur more rarely than literal uses. Also, within metonymies, the reading distribution is skewed towards a few frequent metonymy types. Future task developments should focus on addressing this issue.
- Agirre, E., Màrquez, L., & Wicentowski, R. (Eds.). (2007). Proceedings of the fourth international workshop on semantic evaluations (SemEval-2007). Prague, Czech Republic: Association for Computational Linguistics.
- Barnden, J., Glasbey, S., Lee, M., & Wallington, A. (2003). Domain-transcending mappings in a system for metaphorical reasoning. In Proc. of EACL-2003, pp. 57–61.
- Birke, J., & Sarkaar, A. (2006). A clustering approach for the nearly unsupervised recognition of nonliteral language. In Proceedings of EACL-2006.
- Briscoe, T., & Copestake, A. (1999). Lexical rules in constraint-based grammar. Computational Linguistics, 25(4), 487–526.
- Burnard, L. (1995). Users’ Reference Guide, British National Corpus. Oxford, England: British National Corpus Consortium.
- Carletta, J. (1996). Assessing agreement on classification tasks: The kappa statistic. Computational Linguistics, 22(2), 249–254.
- Clark, S., & Weir, D. (2002). Class-based probability estimation using a semantic hierarchy. Computational Linguistics, 28(2), 187–206.
- Copestake, A., & Briscoe, T. (1995). Semi-productive polysemy and sense extension. Journal of Semantics, 12, 15–67. CrossRef
- Cunningham, H., Maynard, D., Bontcheva, K., & Tablan, V. (2002). GATE: A framework and graphical development environment for robust NLP tools and applications. In Proc. of ACL-2002.
- Fass, D. (1997). Processing metaphor and metonymy. Stanford, CA: Ablex.
- Fellbaum, C. (Ed.). (1998). WordNet: An Electronic Lexical Database. Cambridge, MA: MIT Press.
- Harabagiu, S. (1998). Deriving metonymic coercions from WordNet. In Workshop on the Usage of WordNet in Natural Language Processing Systems, COLING-ACL ’98, Montreal, Canada, pp. 142–148.
- Hobbs, J. R., Stickel, M. E., Appelt, D. E., & Martin, P. (1993). Interpretation as abduction. Artificial Intelligence, 63, 69–142. CrossRef
- Kamei, S.-I., & Wakao, T. (1992). Metonymy: Reassessment, survey of acceptability and its treatment in machine translation systems. In Proc. of ACL-1992, pp. 309–311.
- Krishnakamuran, S., & Zhu, X. (2007). Hunting elusive metaphors using lexical resources. In Proc. of the NAACL-2007 Workshop on Computational Approaches to Figurative Language.
- Lakoff, G., & Johnson, M. (1980). Metaphors we live by. Chicago, IL: Chicago University Press.
- Lapata, M., & Lascarides, A. (2003). A probabilistic account of logical metonymy. Computational Linguistics, 29, 263–317.
- Leveling, J., & Hartrumpf, S. (2006). On metonymy recognition for gir. In Proc. of GIR-2006.
- Levin, B. (1993). English verb classes and alternations. Chicago: University of Chicago Press.
- Markert, K., & Hahn, U. (2002). Understanding metonymies in discourse. Artificial Intelligence, 135(1/2), 145–198. CrossRef
- Markert, K., & Nissim, M. (2002). Metonymy resolution as a classification task. In Proc. of EMNLP-2002, pp. 204–213.
- Markert, K., & Nissim, M. (2006). Metonymic proper names: A corpus-based account. In A. Stefanowitsch (Ed.), Corpora in cognitive linguistics. Vol. 1: Metaphor and metonymy. Berlin: Mouton de Gruyter.
- Martin, J. (1994). Metabank: A knowledge base of metaphoric language conventions. Computational Intelligence, 10(2), 134–149. CrossRef
- Mason, Z. (2004). Cormet: A computational corpus-based conventional metaphor extraction system. Computational Linguistics, 30(1), 23–44. CrossRef
- McCarthy, D., & Carroll, J. (2003). Disambiguating nouns, verbs and adjectives using automatically acquired selectional preferences. Computational Linguistics, 29(4), 639–654. CrossRef
- Nissim, M., & Markert, K. (2003). Syntactic features and word similarity for supervised metonymy resolution. In Proc. of ACL-2003, pp. 56–63.
- Nunberg, G. (1995). Transfers of meaning. Journal of Semantics, 12, 109–132. CrossRef
- Peirsman, Y. (2006). Example-based metonymy recognition for proper nouns. In Student Session of EACL 2006.
- Pustejovsky, J. (1995). The generative lexicon. Cambridge, MA: MIT Press.
- Schuler, K. K. (2005). VerbNet: A broad-coverage, comprehensive verb lexicon. Dissertation, University of Pennsylvania.
- Stallard, D. (1993). Two kinds of metonymy. In Proc. of ACL-1993, pp. 87–94.
- Data and models for metonymy resolution
Language Resources and Evaluation
Volume 43, Issue 2 , pp 123-138
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