Term Definitions Help Hypernymy Detection
Wenpeng Yin, Dan Roth · 2018
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 opendomain testbeds; (ii) Global context from a large corpus and definitions provide complementary information for words.Consequently, our model, HYPERDEF, once trained on taskagnostic data, gets state-of-the-art results in multiple benchmarks 1 . 1 cogcomp.org