Metaphor Detection with Topic Transition, Emotion and Cognition in Context

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.