An LSTM-CRF Based Approach to Token-Level Metaphor Detection

Malay Pramanick, Ashim Gupta, Pabitra Mitra


Abstract
Automatic processing of figurative languages is gaining popularity in NLP community for their ubiquitous nature and increasing volume. In this era of web 2.0, automatic analysis of sarcasm and metaphors is important for their extensive usage. Metaphors are a part of figurative language that compares different concepts, often on a cognitive level. Many approaches have been proposed for automatic detection of metaphors, even using sequential models or neural networks. In this paper, we propose a method for detection of metaphors at the token level using a hybrid model of Bidirectional-LSTM and CRF. We used fewer features, as compared to the previous state-of-the-art sequential model. On experimentation with VUAMC, our method obtained an F-score of 0.674.
Anthology ID:
W18-0908
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:
67–75
Language:
URL:
https://aclanthology.org/W18-0908
DOI:
10.18653/v1/W18-0908
Bibkey:
Cite (ACL):
Malay Pramanick, Ashim Gupta, and Pabitra Mitra. 2018. An LSTM-CRF Based Approach to Token-Level Metaphor Detection. In Proceedings of the Workshop on Figurative Language Processing, pages 67–75, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
An LSTM-CRF Based Approach to Token-Level Metaphor Detection (Pramanick et al., Fig-Lang 2018)
Copy Citation:
PDF:
https://aclanthology.org/W18-0908.pdf