Learning to Negate Adjectives with Bilinear Models

Laura Rimell, Amandla Mabona, Luana Bulat, Douwe Kiela


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
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
71–78
Language:
URL:
https://www.aclweb.org/anthology/E17-2012
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/E17-2012.pdf