Metaphor Detection through Term Relevance


Most computational approaches to metaphor detection try to leverage either conceptual metaphor mappings or selectional preferences. Both require extensive knowledge of the mappings/preferences in question, as well as sufficient data for all involved conceptual domains. Creating these resources is expensive and often limits the scope of these systems.

We propose a statistical approach to metaphor detection that utilizes the rarity of novel metaphors, marking words that do not match a text’s typical vocabulary as metaphor candidates. No knowledge of semantic concepts or the metaphor’s source domain is required.

We analyze the performance of this approach as a stand-alone classifier and as a feature in a machine learning model, reporting improvements in F${}_1$ measure over a random baseline of 58% and 68%, respectively. We also observe that, as a feature, it appears to be particularly useful when data is sparse, while its effect diminishes as the amount of training data increases.

In Proceedings of the 2nd Workshop on Metaphor in NLP (Meta4NLP 2014).