2020/10/06 by Kfir Bar, Nachum Dershowitz, Bar, Kfir +3
Computer Science · Psychology · #Advanced Text Analysis Techniques #Computation and Language (cs.CL) #FOS: Computer and information sciences #Language, Metaphor, and Cognition #Natural Language Processing Techniques #cs.CL
paper · pdf · doi:10.48550/arxiv.2010.02665
Presented at 19th International Conference on Computational Linguistics and Intelligent Text Processing (CICLing), 2018
openalex publication_date 2020/10/06 · arxiv created 2021/12/06 · arxiv updated 2021/12/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.