@inproceedings{yin-roth-2018-term,
title = "Term Definitions Help Hypernymy Detection",
author = "Yin, Wenpeng and
Roth, Dan",
editor = "Nissim, Malvina and
Berant, Jonathan and
Lenci, Alessandro",
booktitle = "Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S18-2025",
doi = "10.18653/v1/S18-2025",
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",
}
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%0 Conference Proceedings
%T Term Definitions Help Hypernymy Detection
%A Yin, Wenpeng
%A Roth, Dan
%Y Nissim, Malvina
%Y Berant, Jonathan
%Y Lenci, Alessandro
%S Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F yin-roth-2018-term
%X 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
%R 10.18653/v1/S18-2025
%U https://aclanthology.org/S18-2025
%U https://doi.org/10.18653/v1/S18-2025
%P 203-213
Markdown (Informal)
[Term Definitions Help Hypernymy Detection](https://aclanthology.org/S18-2025) (Yin & Roth, *SEM 2018)
ACL
- Wenpeng Yin and Dan Roth. 2018. Term Definitions Help Hypernymy Detection. In Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics, pages 203–213, New Orleans, Louisiana. Association for Computational Linguistics.