Relation extraction for coal mine safety information using recurrent neural networks with bidirectional minimal gated unit

Abstract

Abstract The data of coal mine safety field are massive, multi-source and heterogeneous. It is of practical importance to extract information from big data to achieve disaster precaution and emergency response. Existing approaches need to build more features and rely heavily on the linguistic knowledge of researchers, leading to inefficiency, poor portability, and slow update speed. This paper proposes a new relation extraction approach using recurrent neural networks with bidirectional minimal gated unit (MGU) model. This is achieved by adding a back-to-front MGU layer based on original MGU model. It does not require to construct complex text features and can capture the global context information by combining the forward and backward features. Evident from extensive experiments, the proposed approach outperforms the existing initiatives in terms of training time, accuracy, recall and F value.