Measuring Metaphoricity (2014)
Type of ContentConference Contributions - Published
This paper presents the first computationally-derived scalar measurement of metaphoricity. Each input sentence is given a value between 0 and 1 which represents how metaphoric that sentence is. This measure achieves a correlation of 0.450 (Pearson’s R, p <0.01) with an experimental measure of metaphoricity involving human participants. While far from perfect, this scalar measure of metaphoricity allows different thresholds for metaphoricity so that metaphor identification can be fitted for specific tasks and datasets. When reduced to a binary classification evaluation using the VU Amsterdam Metaphor Corpus, the system achieves an F-Measure of 0.608, slightly lower than the comparable binary classification system’s 0.638 and competitive with existing approaches.
CitationDunn J (2014). Measuring Metaphoricity. Association for Computational Linguistics (ACL).
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ANZSRC Fields of Research20 - Language, Communication and Culture::2004 - Linguistics::200402 - Computational Linguistics
47 - Language, communication and culture::4704 - Linguistics::470409 - Linguistic structures (incl. phonology, morphology and syntax)
17 - Psychology and Cognitive Sciences::1702 - Cognitive Science::170204 - Linguistic Processes (incl. Speech Production and Comprehension)
RightsCreative Commons Attribution-NonCommercial-ShareAlike 3.0 International License
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Language-Independent Ensemble Approaches to Metaphor Identification Dunn J; Beltran de Heredia J; Burke M; Gandy L; Kanareykin S; Kapah O; Taylor M; Hines D; Frieder O; Grossman D; Howard N; Koppel M; Morris S; Ortony A; Argamon S (2014)True natural language understanding requires the ability to identify and understand metaphorical utterances, which are ubiquitous in human communication of all kinds. At present, however, even the problem of identifying ...
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