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Data and models for metonymy resolution

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Abstract

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.

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Notes

  1. The example is from the Berkeley Master Metaphor list (http://cogsci.berkeley.edu/lakoff/).

  2. This and all following examples in this paper are from the British National Corpus (BNC) (Burnard 1995). An exception is Ex. 22.

  3. Org-for-members metonymies referring to a spokesperson are quite commonplace so that it is tempting to see them as literal readings. We follow here previous linguistic research (Fass 1997; Lakoff and Johnson 1980) that see these as metonymies.

  4. https://www.cia.gov/cia/publications/factbook/index.html.

  5. FUH results are slightly different from the FUH system paper due to a preprocessing problem in the system, fixed only after the run submission deadline.

  6. This is sometimes enhanced with morphological/syntactic violations such as the plural use for proper names (Copestake and Briscoe 1995) or anaphoric information (Markert and Hahn 2002). However, the basic model relies to a large degree on SRs.

  7. The SUBJ and GRAMM baselines are equal on this subset.

  8. We thank Diana McCarthy for pointing that problem out to us.

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Acknowledgements

We thank the BNC Consortium for allowing us to distribute the extracted samples. We are also grateful to the annotators for the selectional restriction simulations: Ben Hachey, Tim O’Donnell and especially Stephen Clark, who bore the brunt of the annotation. We also had valuable discussions with Diana McCarthy during the preparation of this work.

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Authors and Affiliations

  1. School of Computing, University of Leeds, Woodhouse Lane, LS2 9JT, Leeds, UK

    Katja Markert

  2. Department of Linguistics and Oriental Studies, University of Bologna, via Zamboni 33, 40126, Bologna, Italy

    Malvina Nissim

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  1. Katja Markert
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  2. Malvina Nissim
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Correspondence to Katja Markert.

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Markert, K., Nissim, M. Data and models for metonymy resolution. Lang Resources & Evaluation 43, 123–138 (2009). https://doi.org/10.1007/s10579-009-9087-y

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