基于 BERT‑BiGRU‑CRF 模型的岩土工程实体识别

Geotechnical engineering named entity recognition is an important prerequisite and the work foundation for geotechnical information mining and knowledge Graph. Aiming at the recognition and classification of named entities in geotechnical texts, this article first designs and constructs a named entity corpus of geotechnical engineering according to Standard for Fundamental Terms of Geotechnical Engineering (GB/T 50279-2014) and other national industry standards; and based on deep learning technologies, a named entity recognition and classification deep learning model GENER is proposed for geotechnical engineering text. In GENER, the distributed representation learning of geotechnical engineering text features is realized based on the BERT pretrained language model; the geotechnical engineering text context feature encoding is achieved based on the BiGRU context coding layer; and based on the label decoding layer of CRF, the context features are decoded to generate the label sequence of geotechnical engineering named entity. Finally, based on the geotechnical engineering corpus, the GENER model is experimentally analyzed. comparing with other deep learning models for named entity recognition based on pretrained language models, the GENER model has better performance. The precision reaches 90.94%, the recall reaches 92.88%, the F1 - score reaches 91.89%and model training speed increased by 4.735% respectively.Experiments show that compared with BiLSTM-CRF and CNN-BiLSTM-CRF models, this model is more effective in small-scale corpus geotechnical engineering entity recognition.