Abstract
In this paper we propose a method for detecting metaphorical usage of content terms based on the hypothesis that metaphors can be detected by being characteristic of a different domain than the one they appear in. We formulate the problem as one of extracting knowledge from text classification models, where the latter have been created using standard text classification techniques without any knowledge of metaphor. We then extract from such models a measure of how characteristic of a domain a term is, providing us with a reliable method of identifying terms that are surprising for the context within which they are used. To empirically evaluate our method, we have compiled a corpus of Greek newspaper articles where the training set is only annotated with the broad thematic categories assigned by the newspapers. We have also manually annotated a test corpus with metaphorical word usage. In our experiment, we report results using tf-idf to identify the literal (characteristic) domain of terms and we also analyse the interaction between tf-idf and other typical word features, such as Part of Speech tags.
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Notes
- 1.
Please cf. www.iptc.org for more details.
- 2.
Please see https://bitbucket.org/dataengineering/stemming.
References
Fass, D.: met*: a method for discriminating metonymy and metaphor by computer. Comput. Linguist. 17(1), 49–90 (1991)
Mason, Z.J.: CorMet: a computational, corpus-based conventional metaphor extraction system. Comput. Linguist. 30(1), 23–44 (2004)
Birke, J., Sarkar, A.: A clustering approach for the nearly unsupervised recognition of nonliteral language. In: Proceedings of EACL-2006, Trento, pp. 329–336 (2006)
Krishnakumaran, S., Zhu, X.: Hunting elusive metaphors using lexical resources. In: Proceedings of the Workshop on Computational Approaches to Figurative Language, pp. 13–20, Rochester, Association for Computational Linguistics, April 2007
Shutova, E.: Metaphor identification as interpretation. In: Proceedings of the Second Joint Conference on Lexical and Computational Semantics (*SEM 2013), Atlanta, Georgia, USA, 13–14 June 2013
Schulder, M., Hovy, E.: Metaphor detection through term relevance. In: Proceedings of the 2nd Workshop on Metaphor in NLP, Baltimore, pp. 18–26, 26 June 2014
Kohlschütter, C., Fankhauser, P., Nejdl, W.: Boilerplate detection using shallow text features. In: Proceedings of The Third ACM International Conference on Web Search and Data Mining (WSDM 2010), New York City (2010)
Acknowledgments
The authors are grateful to the annotators for their contribution in preparing the test corpus. We would also like to thank ‘Lefkaditika Nea’ and ‘Thraki’ for granting us permission to use their articles for our research and ‘Avgi’ for offering its content under a creative commons license.
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Pechlivanis, K., Konstantopoulos, S. (2015). Corpus Based Methods for Learning Models of Metaphor in Modern Greek. In: Dediu, AH., Martín-Vide, C., Vicsi, K. (eds) Statistical Language and Speech Processing. SLSP 2015. Lecture Notes in Computer Science(), vol 9449. Springer, Cham. https://doi.org/10.1007/978-3-319-25789-1_21
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DOI: https://doi.org/10.1007/978-3-319-25789-1_21
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