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Bridging the Defined and the Defining: Exploiting Implicit Lexical Semantic Relations in Definition Modeling
lib:f107f4fc8bb487a9 (v1.0.0)
Authors: Koki Washio,Satoshi Sekine,Tsuneaki Kato
Where published: IJCNLP 2019 11
Document: PDF DOI
Abstract URL: https://www.aclweb.org/anthology/D19-1357/
Definition modeling includes acquiring word embeddings from dictionary definitions and generating definitions of words. While the meanings of defining words are important in dictionary definitions, it is crucial to capture the lexical semantic relations between defined words and defining words. However, thus far, the utilization of such relations has not been explored for definition modeling. In this paper, we propose definition modeling methods that use lexical semantic relations. To utilize implicit semantic relations in definitions, we use unsupervisedly obtained pattern-based word-pair embeddings that represent semantic relations of word pairs. Experimental results indicate that our methods improve the performance in learning embeddings from definitions, as well as definition generation.
Where published: IJCNLP 2019 11
Document: PDF DOI
Abstract URL: https://www.aclweb.org/anthology/D19-1357/
Definition modeling includes acquiring word embeddings from dictionary definitions and generating definitions of words. While the meanings of defining words are important in dictionary definitions, it is crucial to capture the lexical semantic relations between defined words and defining words. However, thus far, the utilization of such relations has not been explored for definition modeling. In this paper, we propose definition modeling methods that use lexical semantic relations. To utilize implicit semantic relations in definitions, we use unsupervisedly obtained pattern-based word-pair embeddings that represent semantic relations of word pairs. Experimental results indicate that our methods improve the performance in learning embeddings from definitions, as well as definition generation.
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