SemEval-2007 Task 08: Metonymy Resolution at SemEval-2007

Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval-2007), pages 36–41, Prague, June 2007. c⃝2007 Association for Computational Linguistics SemEval-2007 Task 08: Metonymy Resolution at SemEval-2007 Katja Markert School of Computing University of Leeds, UK markert@comp.leeds.ac.uk Malvina Nissim Dept. of Linguistics and Oriental Studies University of Bologna, Italy malvina.nissim@unibo.it Abstract We provide an overview of the metonymy resolution shared task organised within SemEval-2007. We describe the problem, the data provided to participants, and the evaluation measures we used to assess per- formance. We also give an overview of the systems that have taken part in the task, and discuss possible directions for future work. 1 Introduction Both word sense disambiguation and named entity recognition have benefited enormously from shared task evaluations, for example in the Senseval, MUC and CoNLL frameworks. Similar campaigns have not been developed for the resolution of figurative language, such as metaphor, metonymy, idioms and irony. However, resolution of figurative language is an important complement to and extension of word sense disambiguation as it often deals with word senses that are not listed in the lexicon. For exam- ple, the meaning of stopover in the sentence He saw teaching as a stopover on his way to bigger things is a metaphorical sense of the sense “stopping place in a physical journey”, with the literal sense listed in WordNet 2.0 but the metaphorical one not being listed.1 The same holds for the metonymic reading of rattlesnake (for the animal’s meat) in Roast rat- tlesnake tastes like chicken.2 Again, the meat read- 1This example was taken from the Berkely Master Metaphor list (Lakoff and Johnson, 1980) . 2From now on, all examples in this paper are taken from the British National Corpus (BNC) (Burnard, 1995), but Ex. 23. ing of rattlesnake is not listed in WordNet whereas the meat reading for chicken is. As there is no common framework or corpus for figurative language resolution, previous computa- tional works (Fass, 1997; Hobbs et al., 1993; Barn- den et al., 2003, among others) carry out only small- scale evaluations. In recent years, there has been growing interest in metaphor and metonymy resolu- tion that is either corpus-based or evaluated on larger datasets (Martin, 1994; Nissim and Markert, 2003; Mason, 2004; Peirsman, 2006; Birke and Sarkaar, 2006; Krishnakamuran and Zhu, 2007). Still, apart from (Nissim and Markert, 2003; Peirsman, 2006) who evaluate their work on the same dataset, results are hardly comparable as they all operate within dif- ferent frameworks. This situation motivated us to organise the first shared task for figurative language, concentrating on metonymy. In metonymy one expression is used to refer to the referent of a related one, like the use of an animal name for its meat. Similarly, in Ex. 1, Vietnam, the name of a location, refers to an event (a war) that happened there. (1) Sex, drugs, and Vietnam have haunted Bill Clinton’s campaign. In Ex. 2 and 3, BMW, the name of a company, stands for its index on the stock market, or …

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