Abstract

Metaphor is a common linguistic tool in communication, making its detection in discourse a crucial task for natural language understanding. One popular approach to this challenge is to capture semantic incohesion between a metaphor and the dominant topic of the surrounding text. While these methods are effective, they tend to overclassify target words as metaphorical when they deviate in meaning from its context. We present a new approach that (1) distinguishes literal and non-literal use of target words by examining sentence-level topic transitions and (2) captures the motivation of speakers to express emotions and abstract concepts metaphorically. Experiments on an online breast cancer discussion forum dataset demonstrate a significant improvement in metaphor detection over the state-of-theart. These experimental results also reveal a tendency toward metaphor usage in personal topics and certain emotional contexts.

Highlights

  • Figurative language is commonly used in human communication ranging from literature to everyday speech

  • (3) through our empirical evaluation, we find that metaphor occurs more frequently around personal topics

  • Using Sentence Latent Dirichlet Allocation (LDA), we modeled four features to capture how the topic changes around the sentence where a target word resides

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Summary

Introduction

Figurative language is commonly used in human communication ranging from literature to everyday speech. Previous approaches to modeling metaphor have used either the semantic and syntactic information in just the sentence that contains a metaphor (Turney et al, 2011; Tsvetkov et al, 2014), or the context beyond a single sentence (Broadwell et al, 2013; Strzalkowski et al, 2013; Schulder and Hovy, 2014; Klebanov et al, 2015; Jang et al, 2015) to detect topical discrepancy between a candidate metaphor and the dominant theme (See Section 2 for more detailed literature review). Previous context-based models tend to overclassify literal words as metaphorical if they find semantic contrast with the governing context. These cases manifested in the work by Schulder and Hovy (2014) and Jang et al (2015) as high recall but low precision for metaphorical instances

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