Event extraction technology of training documents based on deep learning

A large amount of textual information will be generated in the exercise training process, and enormous cognitive pressure will be exerted on training evaluators due to complexity and diversity of such information.How to fully extract unstructured data from exercise training documents and provide efficient services for analysis and evaluation personnel is a challenging issue in data processing. In this paper, we propose a deep learning-based event extraction technique for exercise training documents, which addresses the characteristics of abundant professional terminology, coexistence of Chinese and English, and dense key information in short sentences. By leveraging the powerful text feature extraction of ALBERT and the structured prediction of CRF sequence labeling, we construct an event extraction model for exercise training documents. Experimental results on the training data set demonstrate that this model performs well in text extraction and has practical applications for extracting information from exercise training documents.