Abstract
This study first examines the implicit and explicit premises of four systems for identifying metaphoric utterances from unannotated input text. All four systems are then evaluated on a common data set in order to see which premises are most successful. The goal is to see if these systems can find metaphors in a corpus that is mostly non-metaphoric without over-identifying literal and humorous utterances as metaphors. Three of the systems are distributional semantic systems, including a source-target mapping method [1-4]; a word abstractness measurement method [5], [6, 7]; and a semantic similarity measurement method [8, 9]. The fourth is a knowledge-based system which uses a domain interaction method based on the SUMO ontology [10, 11], implementing the hypothesis that metaphor is a product of the interactions among all of the concepts represented in an utterance [12, 13].
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Dunn, J. (2013). Evaluating the Premises and Results of Four Metaphor Identification Systems. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2013. Lecture Notes in Computer Science, vol 7816. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37247-6_38
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DOI: https://doi.org/10.1007/978-3-642-37247-6_38
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