Improving Hypernymy Prediction via Taxonomy Enhanced Adversarial Learning

Abstract

Hypernymy is a basic semantic relation in computational linguistics that expresses the "is-a"relation between a generic concept and its specific instances, serving as the backbone in taxonomies and ontologies. Although several NLP tasks related to hypernymy prediction have been extensively addressed, few methods have fully exploited the large number of hypernymy relations in Web-scale taxonomies. In this paper, we introduce the Taxonomy Enhanced Adversarial Learning (TEAL) for hypernymy prediction. We first propose an unsupervised measure U-TEAL to distinguish hypernymy with other semantic relations. It is implemented based on a word embedding projection network distantly trained over a taxonomy. To address supervised hypernymy detection tasks, the supervised model S-TEAL and its improved version, the adversarial supervised model AS-TEAL, are further presented. Specifically, AS-TEAL employs a coupled adversarial training algorithm to transfer hierarchical knowledge in taxonomies to hypernymy prediction models. We conduct extensive experiments to confirm the effectiveness of TEAL over three standard NLP tasks: unsupervised hypernymy classification, supervised hypernymy detection and graded lexical entailment. We also show that TEAL can be applied to non-English languages and can detect missing hypernymy relations in taxonomies.