Learning to Negate Adjectives with Bilinear Models
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.
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