Metaphor Interpretation Using Word Embeddings
This addresses the problem of automated metaphor understanding for natural language processing applications, though it appears incremental as it builds on existing word embedding and corpus-based methods.
The paper tackles metaphor interpretation by developing a model that uses word embeddings to generate ranked lists of interpretations for nominal metaphors like 'time is money', achieving encouraging preliminary results on annotated datasets.
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.