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Published in:
2013 | OriginalPaper | Chapter
Using Imageability and Topic Chaining to Locate Metaphors in Linguistic Corpora
Authors : George Aaron Broadwell, Umit Boz, Ignacio Cases, Tomek Strzalkowski, Laurie Feldman, Sarah Taylor, Samira Shaikh, Ting Liu, Kit Cho, Nick Webb
Published in: Social Computing, Behavioral-Cultural Modeling and Prediction
Publisher: Springer Berlin Heidelberg
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The reliable automated identification of metaphors still remains a challenge in metaphor research due to ambiguity between semantic and contextual interpretation of individual lexical items. In this article, we describe a novel approach to metaphor identification which is based on three intersecting methods:
imageability
,
topic chaining
, and
semantic clustering
. Our hypothesis is that metaphors are likely to use highly imageable words that do not generally have a topical or semantic association with the surrounding context. Our method is thus the following: (1) identify the highly imageable portions of a paragraph, using psycholinguistic measures of imageability, (2) exclude imageability peaks that are part of a topic chain, and (3) exclude imageability peaks that show a semantic relationship to the main topics. We are currently working towards fully automating this method for a number of languages.