Abstract
The contrast between the contextual and general meaning of a word serves as an important clue for detecting its metaphoricity. In this paper, we present a deep neural architecture for metaphor detection which exploits this contrast. Additionally, we also use cost-sensitive learning by re-weighting examples, and baseline features like concreteness ratings, POS and WordNet-based features. The best performing system of ours achieves an overall F1 score of 0.570 on All POS category and 0.605 on the Verbs category at the Metaphor Shared Task 2018.- Anthology ID:
- W18-0914
- 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:
- 115–120
- Language:
- URL:
- https://aclanthology.org/W18-0914
- DOI:
- 10.18653/v1/W18-0914
- Cite (ACL):
- Krishnkant Swarnkar and Anil Kumar Singh. 2018. Di-LSTM Contrast : A Deep Neural Network for Metaphor Detection. In Proceedings of the Workshop on Figurative Language Processing, pages 115–120, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- Di-LSTM Contrast : A Deep Neural Network for Metaphor Detection (Swarnkar & Singh, Fig-Lang 2018)
- PDF:
- https://preview.aclanthology.org/ingest-2024-clasp/W18-0914.pdf