Joint Big Data Extraction Method for Coal Mine Safety with Characters and Words Fusion

The entity relation extraction of coal mine safety accidents is of great significance to the supervision and prevention of coal mine safety accidents. Aiming at the entity relation joint extraction of coal mine safety accidents, this paper proposes an entity relation joint extraction model based on multi-heads attention and characters and words fusion. On the basis of using the Chinese pre-training models RoBERTa-wwm-ext and Word2vec to generate character vectors and word vectors respectively, the multi-heads self-attention mechanism is used to assign more weights to related word vectors, and obtain the dependencies between distant entities and the semantic relations of entities from different perspectives. Then character vectors and word vectors are spliced into BiLSTM, and finally the label sequence is output by Conditional Random Field (CRF). In the field of entity relation extraction, the overlapping entity relation extraction and reconciliation of coal mine safety accidents are more difficult. The harmonic mean value is 93.19 % , and the effect of extracting the entity relation of coal mine safety accidents has been significantly improved. The overall harmonic mean value is 94.54 % , which is better than the comparison models.