基于BERT的施工安全事故文本命名实体识别方法

Named Entity Recognition Method of Construction Safety Accident Text Based on BERT

为解决传统施工安全管理中对事故报告信息分析效率低的问题,利用自然语言处理(Natural Language Processing,NLP)技术,提出基于双向编码器表示(Bidirectional Encoder Representations from Transformers,BERT)的施工安全事故文本命名实体识别方法。以自建的施工安全事故领域实体标注语料数据集为研究对象,首先利用BERT预训练模型获取动态字向量,然后采用双向长短时记忆网络-注意力机制-条件随机场(BiLSTM-Attention-CRF)对前一层输出的语义编码进行序列标注和解码以获取最优文本标签序列。实验结果表明,该模型在自建数据集上的F1值分数为92.58%,较基准模型BiLSTM-CRF提升了4.19%;该方法对事故时间等5类实体识别F1值均可达到91%以上,验证了该方法对施工安全事故实体识别的有效性,说明模型可用于实际施工知识管理中并指导建筑安全管理的安全培训。

In order to solve the problem of low efficiency of accident report information analysis in traditional construction safety management, a BERT-based construction safety accident text named entity recognition method was proposed using Natural Language Processing(NLP) technology. A self-built corpus dataset of entity annotation in the field of construction safety accidents was used as the re-search object. Firstly, Bidirectional Encoder Representations from Transformers(BERT) pre-training model was used to obtain dynamic word vectors, and then used Bidirectional Long Short Term Memory-Attention-Conditional Random Field(BiLSTMAttention-CRF) to sequentially annotate and decode the semantic codes output from the previous layer to obtain the optimal text label sequences. The experimental results showed that the F1 value score of the model on the self-built dataset was 92.58%, which was 4.19%higher than the benchmark model BiLSTM-CRF;the method achieved an F1 value of 91% or more for the recognition of five types of entities such as accident time, which verified the effectiveness of the method for the recognition of construction safety accident entities.It indicated that the model can be used in practical construction knowledge management and guide safety training for construction safety management.