Metaphor Interpretation Using Word Embeddings

Kfir Bar, Nachum Dershowitz, Lena Dankin


We suggest a model for metaphor interpretation using word embeddings trained over a relatively large corpus. Our system handles nominal metaphors, like time is money. It generates a ranked list of potential interpretations of given metaphors. Candidate meanings are drawn from collocations of the topic (time) and vehicle (money) components, automatically extracted from a dependency-parsed corpus. We explore adding candidates derived from word association norms (common human responses to cues). Our ranking procedure considers similarity between candidate interpretations and metaphor components, measured in a semantic vector space. Lastly, a clustering algorithm removes semantically related duplicates, thereby allowing other candidate interpretations to attain higher rank. We evaluate using different sets of annotated metaphors, with encouraging preliminary results.


Metaphor interpretation, word embeddings

Full Text: PDF