Abstract
Metaphor is an essential element of human cognition which is often used to express ideas and emotions that might be difficult to express using literal language. Processing metaphoric language is a challenging task for a wide range of applications ranging from text simplification to psychotherapy. Despite the variety of approaches that are trying to process metaphor, there is still a need for better models that mimic the human cognition while exploiting fewer resources. In this paper, we present an approach based on distributional semantics to identify metaphors on the phrase-level. We investigated the use of different word embeddings models to identify verb-noun pairs where the verb is used metaphorically. Several experiments are conducted to show the performance of the proposed approach on benchmark datasets.- Anthology ID:
- W18-0910
- Volume:
- Proceedings of the Workshop on Figurative Language Processing
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Editors:
- Beata Beigman Klebanov, Ekaterina Shutova, Patricia Lichtenstein, Smaranda Muresan, Chee Wee
- Venue:
- Fig-Lang
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 81–90
- Language:
- URL:
- https://aclanthology.org/W18-0910
- DOI:
- 10.18653/v1/W18-0910
- Cite (ACL):
- Omnia Zayed, John Philip McCrae, and Paul Buitelaar. 2018. Phrase-Level Metaphor Identification Using Distributed Representations of Word Meaning. In Proceedings of the Workshop on Figurative Language Processing, pages 81–90, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- Phrase-Level Metaphor Identification Using Distributed Representations of Word Meaning (Zayed et al., Fig-Lang 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/W18-0910.pdf