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.
neural pattern-based word-pair embedding models (Washio and Kato, 2018a,b;Joshi et al., 2019) unsupervisedly learn two neural networks: a word-pair encoder and pattern encoder, both of which encode the word-pair and lexico-syntactic pattern respectively into the same embedding space.