Current approaches to supervised learning of metaphor tend to use sophisticated features and restrict their attention to constructions and contexts where these features apply. In this paper, we describe the development of a supervised learning system to classify all content words in a running text as either being used metaphorically or not. We start by examining the performance of a simple unigram baseline that achieves surprisingly good results for some of the datasets. We then show how the recall of the system can be improved over this strong baseline.
Beigman REF propose a supervised approach to predict the metaphoricity of all content words with any part-of-speech in a running text.
Different Texts, Same Metaphors: Unigrams and Beyond
Proceedings of the Second Workshop on Metaphor in NLP
Proceedings of the Second Workshop on Metaphor in NLP
Computer Science