Literal and metaphorical sense identification through concrete and abstract context

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Proceedings titleProceedings of the 2011 Conference on the Empirical Methods in Natural Language Processing
ConferenceEmpirical Methods in Natural Language Processing (EMNLP 2011), July 27-31, 2011, Edinburgh, Scotland, UK
Pages680690; # of pages: 11
AbstractMetaphor is ubiquitous in text, even in highly technical text. Correct inference about textual entailment requires computers to distinguish the literal and metaphorical senses of a word. Past work has treated this problem as a classical word sense disambiguation task. In this paper, we take a new approach, based on research in cognitive linguistics that views metaphor as a method for transferring knowledge from a familiar, well-understood, or concrete domain to an unfamiliar, less understood, or more abstract domain. This view leads to the hypothesis that metaphorical word usage is correlated with the degree of abstractness of the word’s context. We introduce an algorithm that uses this hypothesis to classify a word sense in a given context as either literal (denotative) or metaphorical (connotative). We evaluate this algorithm with a set of adjectivenoun phrases (e.g., in dark comedy, the adjective dark is used metaphorically; in dark hair, it is used literally) and with the TroFi (Trope Finder) Example Base of literal and nonliteral usage for fifty verbs. We achieve state-of-theart performance on both datasets.
Publication date
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NPARC number18533382
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Record identifier1152ebbb-654f-4e3a-8ace-7d41c209a34c
Record created2011-09-03
Record modified2016-05-09
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