Word Embedding and WordNet Based Metaphor Identification and Interpretation

Rui Mao, Chenghua Lin, Frank Guerin


Abstract
Metaphoric expressions are widespread in natural language, posing a significant challenge for various natural language processing tasks such as Machine Translation. Current word embedding based metaphor identification models cannot identify the exact metaphorical words within a sentence. In this paper, we propose an unsupervised learning method that identifies and interprets metaphors at word-level without any preprocessing, outperforming strong baselines in the metaphor identification task. Our model extends to interpret the identified metaphors, paraphrasing them into their literal counterparts, so that they can be better translated by machines. We evaluated this with two popular translation systems for English to Chinese, showing that our model improved the systems significantly.
Anthology ID:
P18-1113
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Editors:
Iryna Gurevych, Yusuke Miyao
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1222–1231
Language:
URL:
https://aclanthology.org/P18-1113
DOI:
10.18653/v1/P18-1113
Bibkey:
Cite (ACL):
Rui Mao, Chenghua Lin, and Frank Guerin. 2018. Word Embedding and WordNet Based Metaphor Identification and Interpretation. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1222–1231, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Word Embedding and WordNet Based Metaphor Identification and Interpretation (Mao et al., ACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/P18-1113.pdf
Presentation:
 P18-1113.Presentation.pdf
Video:
 https://aclanthology.org/P18-1113.mp4