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

  • Affiliations:

  • State University of New York - University at Albany, NY;State University of New York - University at Albany, NY;State University of New York - University at Albany, NY;State University of New York - University at Albany, NY;State University of New York - University at Albany, NY;Sarah M. Taylor Consulting, LLC;State University of New York - University at Albany, NY;State University of New York - University at Albany, NY;State University of New York - University at Albany, NY;Union College, Schenectady, New York

  • Venue:
  • SBP'13 Proceedings of the 6th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
  • Year:
  • 2013

Quantified Score

Hi-index 0.00

Visualization

Abstract

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.