The Journal of Machine Learning Research
met*: a method for discriminating metonymy and metaphor by computer
Computational Linguistics
CorMet: a computational, corpus-based conventional metaphor extraction system
Computational Linguistics
Corpus-driven metaphor harvesting
FigLanguages '07 Proceedings of the Workshop on Computational Approaches to Figurative Language
Hunting elusive metaphors using lexical resources
FigLanguages '07 Proceedings of the Workshop on Computational Approaches to Figurative Language
HumanJudge '08 Proceedings of the Workshop on Human Judgements in Computational Linguistics
ScaNaLU '06 Proceedings of the Third Workshop on Scalable Natural Language Understanding
Topic models for word sense disambiguation and token-based idiom detection
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
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Using metaphor-annotated material that is sufficiently representative of the topical composition of a similar-length document in a large background corpus, we show that words expressing a discourse-wide topic of discussion are less likely to be metaphorical than other words in a document. Our results suggest that to harvest metaphors more effectively, one is advised to consider words that do not represent a discourse topic.