Abstract
We learn a mapping that negates adjectives by predicting an adjective’s antonym in an arbitrary word embedding model. We show that both linear models and neural networks improve on this task when they have access to a vector representing the semantic domain of the input word, e.g. a centroid of temperature words when predicting the antonym of ‘cold’. We introduce a continuous class-conditional bilinear neural network which is able to negate adjectives with high precision.- Anthology ID:
- E17-2012
- Volume:
- Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
- Month:
- April
- Year:
- 2017
- Address:
- Valencia, Spain
- Editors:
- Mirella Lapata, Phil Blunsom, Alexander Koller
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 71–78
- Language:
- URL:
- https://aclanthology.org/E17-2012
- DOI:
- Cite (ACL):
- Laura Rimell, Amandla Mabona, Luana Bulat, and Douwe Kiela. 2017. Learning to Negate Adjectives with Bilinear Models. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 71–78, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- Learning to Negate Adjectives with Bilinear Models (Rimell et al., EACL 2017)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/E17-2012.pdf