@InProceedings{yin-roth:2018:S18-2,
  author    = {Yin, Wenpeng  and  Roth, Dan},
  title     = {Term Definitions Help Hypernymy Detection},
  booktitle = {Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, Louisiana},
  publisher = {Association for Computational Linguistics},
  pages     = {203--213},
  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},
  url       = {http://www.aclweb.org/anthology/S18-2025}
}