SelfORE: Self-supervised Relational Feature Learning for Open Relation Extraction
Xuming Hu, Lijie Wen, Yusong Xu, Chenwei Zhang, Philip S. Yu · 2020
Open relation extraction is the task of extracting open-domain relation facts from natural language sentences.Existing works either utilize heuristics or distant-supervised annotations to train a supervised classifier over pre-defined relations, or adopt unsupervised methods with additional assumptions that have less discriminative power.In this work, we propose a self-supervised framework named SelfORE, which exploits weak, self-supervised signals by leveraging large pretrained language model for adaptive clustering on contextualized relational features, and bootstraps the self-supervised signals by improving contextualized features in relation classification.Experimental results on three datasets show the effectiveness and robustness of SelfORE on open-domain Relation Extraction when comparing with competitive baselines.Source code is available 1 .