Pre-Trained BERT-GRU Model for Relation Extraction
Rongli Yi, Wenxin Hu · 2019
Existing works on entity relation extraction are based on neural networks and achieve state-of-the art performance by leveraging extra lexical and syntactic features from external NLP pre-processing tools. Feature based methods are hard to be generalized in new language, and the pre-processing procedure may lead to additional error. To overcome this problem, we propose BERT-GRU(Bidirectional Encoder Representations from Transformer with Bidirectional Gated Recurrent Unit), which exploits pre-trained deep language representations to obtain the latent linguistic information for relation extraction and without using any high-level linguistic resources extracted by NLP tools. We conduct our experiment on GPU environment which can enhance the training procedure, results on SemEval-2010 task 8 show that our model outperforms existing methods without any external features.