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.
Highlights
5G communication technology with low latency and great bandwidth accelerates the data transmission rate, enabling the use of real-time data to achieve the perception of Industrial Internet of Things (IIoT) environment [1, 2]
The reason is that the automation and information systems in the coal mine are independent of each other and the data are not interconnected [3]. Another reason is that the coal mine safety field involves people, devices, environment and management, and the resulting data are massive, multi-source and heterogeneous, whereas insight and knowledge are hidden within these big data
We design an automatic relation extraction approach using recurrent neural networks (RNNs) with bidirectional minimal gated unit (Bi-MGU) model to capture the global context information. This is achieved by adding a back-to-front MGU layer based on original MGU model
Summary
The reason is that the automation and information systems in the coal mine are independent of each other and the data are not interconnected [3]. Another reason is that the coal mine safety field involves people, devices, environment and management, and the resulting data are massive, multi-source and heterogeneous, whereas insight and knowledge are hidden within these big data. How to extract and predict information from big data, support cross-system information sharing, and achieve diversified suggestions are of great significance for disaster prevention and emergency response in the process of coal mine production [4, 5, 6]
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