Data and models for metonymy resolution |
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Authors: | Katja Markert Malvina Nissim |
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Affiliation: | (1) School of Computing, University of Leeds, Woodhouse Lane, LS2 9JT Leeds, UK;(2) Department of Linguistics and Oriental Studies, University of Bologna, via Zamboni 33, 40126 Bologna, Italy |
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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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Keywords: | Metonymy Selectional restrictions Shared task evaluation |
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