A Distantly-Supervised Relation Extraction Method Based on Selective Gate and Noise Correction

Chen Zhuowei, Tian Yujia, Wang Lianxi, Jiang Shengyi

Abstract

“Entity relation extraction, as a core task of information extraction, aims to predict the relation ofentity pairs identified by text, and its research results are applied to various fields. To addressthe problem that current distantly supervised relation extraction (DSRE) methods based on large-scale corpus annotation generate a large amount of noisy data, a DSRE method that incorporatesselective gate and noise correction framework is proposed. The selective gate is used to reason-ably select the sentence features in the sentence bag, while the noise correction is used to correctthe labels of small classes of samples that are misclassified into large classes during the modeltraining process, to reduce the negative impact of noisy data on relation extraction. The resultson the English datasets clearly demonstrate that our proposed method outperforms other base-line models. Moreover, the experimental results on the Chinese dataset indicate that our methodsurpasses other models, providing further evidence that our proposed method is both robust andeffective.”