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Supervised Information Extraction of Chinese Equipment Maintenance Documents

The service data of equipment maintenance is scattered, complex and uncorrelated, and depends on the experience of experts. This paper focuses on the knowledge extraction technology of equipment maintenance documents. However, equipment maintenance documents are characterized by no obvious boundary symbols, many professional terms, and rich context information. Therefore, Convolution Neural Network - Bi-directional Long Short-Term Memory - Conditional Random Field(CNN-BiLSTM-CRF) entity extraction model is designed in this paper on the basis of Bi-directional Long Short-Term Memory – Conditional Random Field(BiLSTM-CRF) model. Aiming at the normative and implicit characteristics of equipment documents, this paper designed a relationship extraction method based on the fusion of pattern and CNN. Experimental results show that both models have good results.