Metaphor Detection with Topic Transition, Emotion and Cognition in Context

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.

A Corpus of Non-Native Written English Annotated for Metaphor

“…Recently, researchers also reported experiments on a corpus of proverbs (Özbal et al, 2016), a corpus of posts to an online breast cancer support group (Jang et al, 2016(Jang et al, , 2015, and on argumentative essays (Beigman Klebanov et al, 2015); in these studies, feature sets originally developed for the VUA corpus served as baselines. We follow the same methodology.…”

Finding Structure in Figurative Language: Metaphor Detection with Topic-based Frames

“…There are many different types of metaphor including metaphors that do not violate any local linguistic expectations (Jang et al, 2015(Jang et al, , 2016. In order to find other patterns not predicated on the assumption of constraint violation, one might investigate which domains are frequently mapped metaphorically, or what target and source domains are frequently used together in metaphors.…”

“…In addition, within a sentence, they used the idea that interplay between the target words category and that of other words is indicative of the non-literalness of the target word. Jang et al (2016), building on the work of Jang et al (2015), more aggressively tackle the problem that distinguishes metaphorical/literal usage when there has been a recent topic transition. They do so by modeling topic transitions in conjunction with situational context.…”

“…This limited conceptualization of metaphor within these restricted contexts has allowed prior work to leverage local indicators to identify metaphorical language, such as the violation of selectional preferences (Martin, 1996;Shutova et al, 2010;Huang, 2014) or the use of abstract vs concrete descriptors (Turney et al, 2011;Brysbaert et al, 2014;Tsvetkov et al, 2013). When detecting metaphor in an extended discourse, and especially for the purpose of modeling the use of metaphor in interaction, however, a broader conceptualization of metaphor is needed in order to accommodate the many places where these simplifying assumptions break down (Jang et al, 2015(Jang et al, , 2016. Detection of metaphors in naturalistic discourse remains an open problem.…”

Proceedings of the Second Workshop on NLP and Computational Social Science

“…Recent computational analyses of style, metaphor, framing and politeness have investigated how language is used to achieve social goals in online communities Jang et al, 2016;. We examine CS in a similar fashion.…”