Abstract
Metaphor is a popular figure of speech. Popularity of metaphors calls for their automatic identification and interpretation. Most of the unsupervised methods directed at detection of metaphors use some hand-coded knowledge. We propose an unsupervised framework for metaphor detection that does not require any hand-coded knowledge. We applied clustering on features derived from Adjective-Noun pairs for classifying them into two disjoint classes. We experimented with adjective-noun pairs of a popular dataset annotated for metaphors and obtained an accuracy of 72.87% with k-means clustering algorithm.- Anthology ID:
- W18-0909
- 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:
- 76–80
- Language:
- URL:
- https://aclanthology.org/W18-0909
- DOI:
- 10.18653/v1/W18-0909
- Cite (ACL):
- Malay Pramanick and Pabitra Mitra. 2018. Unsupervised Detection of Metaphorical Adjective-Noun Pairs. In Proceedings of the Workshop on Figurative Language Processing, pages 76–80, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- Unsupervised Detection of Metaphorical Adjective-Noun Pairs (Pramanick & Mitra, Fig-Lang 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/W18-0909.pdf