Hypernym Discovery via a Recurrent Mapping Model
Yuhang Bai, Richong Zhang, Fanshuang Kong, Junfan Chen, Yongyi Mao · 2021
Hypernym discovery aims to identify all possible hypernyms of a given term.The most recent hypernym discovery models exploit multiple mapping functions to project a term to different semantic spaces and then aggregate these embeddings to a general representation for further classification.We refer to this model as a parallel style model.In this work, we observe that there are hierarchical relations between a target terms' hypernyms.However, these hierarchical relations were not sufficiently considered in the previous parallel style model.To leverage the hierarchical relations, we propose a sequential style model that recurrently maps the query words to their hypernyms, starting from the most specific ones to the less specific ones.Empirical studies on SemEval-2018 Task 9 confirm the effectiveness of the presented model.