Term Definitions Help Hypernymy Detection

TL;DR

This paper introduces HyperDef, a novel hypernymy detection method that encodes word definitions and context-driven representations, enabling better generalization and state-of-the-art performance across benchmarks.

Contribution

The paper proposes HyperDef, a new paradigm that uses definitional sentences and context to improve hypernymy detection, overcoming corpus limitations of previous methods.

Findings

HyperDef achieves state-of-the-art results on multiple benchmarks.

Definitional sentences provide strong, corpus-independent word representations.

Combining definitions with global context enhances hypernymy detection accuracy.

Abstract

Existing methods of hypernymy detection mainly rely on statistics over a big corpus, either mining some co-occurring patterns like "animals such as cats" or embedding words of interest into context-aware vectors. These approaches are therefore limited by the availability of a large enough corpus that can cover all terms of interest and provide sufficient contextual information to represent their meaning. In this work, we propose a new paradigm, HyperDef, for hypernymy detection -- expressing word meaning by encoding word definitions, along with context driven representation. This has two main benefits: (i) Definitional sentences express (sense-specific) corpus-independent meanings of words, hence definition-driven approaches enable strong generalization -- once trained, the model is expected to work well in open-domain testbeds; (ii) Global context from a large corpus and definitions provide complementary information for words. Consequently, our model, HyperDef, once trained on task-agnostic data, gets state-of-the-art results in multiple benchmarks