An earthquake disaster mitigation entity recognition method that integrates attention and word boundary
In response to the problem of insufficient feature information and low recognition efficiency in the task of naming entities for earthquake prevention and disaster reduction,this study proposes a method for entity recognition in the field of earthquake prevention and disaster reduction that integrates Self-Attention and MarkBERT. Using MarkBERT to introduce word boundary information during the pre-training process,a sequence containing boundary information is obtained;Obtain character position information through BiLSTM;Introducing a Self-Attention mechanism to further capture the internal relationships of sequences and allocate feature weights;Finally,the optimal sequence annotation result is output through conditional random fields. This model was tested based on the"BIO annotation data of earthquake prevention and control related questions",and the F1 value reached 96. 18％. And the superiority of the algorithm was verified by comparing three sets of similar models. The experimental results show that the model can efficiently and accurately identify earthquake prevention and disaster reduction entities in text.
named entity recognitionnatural language processingearthquake prevention and disaster reductionMarkBERTself-attention mechanismBiLSTMCRF
万方数据
维普
