Unsupervised hypernymy directionality prediction using context terms

Abstract

Hypernymy directionality prediction is an important task in Natural Language Processing (NLP) due to its significant usages in natural language understanding and generation. Many supervised and unsupervised methods have been proposed for this task. Supervised methods require labeled examples, which are not readily available for many domains; besides, supervised models for this task that are trained on data from one domain performs poorly on data in a different domain. Therefore, unsupervised methods that are universally applicable for all domains are preferred. Existing unsupervised methods for hypernymy directionality prediction are outdated and suffer from poor performance. Specifically, they do not leverage distributional pre-trained vectors from neural language models, which have shown to be very effective in diverse NLP tasks. In this paper, we present DECIDE, a simple yet effective unsupervised method for hypernymy directionality prediction that exploits neural pre-trained vectors of words in context. By utilizing the distributional informativeness hypothesis over the context vectors, DECIDE predicts the hypernym directionality between a pair of words with a high accuracy. Extensive experiments on seven datasets demonstrate that DECIDE outperforms or achieves comparable performance to existing unsupervised and supervised methods.