| 1 | End End-to to-End Chinese Entity Recognition Based on BERT BERT-BiLSTM BiLSTM-ATT ATT-CRF显示文摘Traditional named entity recognition methods need professional domain knowl-edge and a large amount of human participation to extract features,as well as the Chinese named entity recognition method based on a neural network model,which brings the prob-lem that vector representation is too singular in the process of character vector representa-tion.To solve the above problem,we propose a Chinese named entity recognition method based on the BERT-BiLSTM-ATT-CRF model.Firstly,we use the bidirectional encoder representations from transformers(BERT)pre-training language model to obtain the se-mantic vector of the word according to the context information of the word;Secondly,the word vectors trained by BERT are input into the bidirectional long-term and short-term memory network embedded with attention mechanism(BiLSTM-ATT)to capture the most important semantic information in the sentence;Finally,the conditional random field(CRF)is used to learn the dependence between adjacent tags to obtain the global optimal sentence level tag sequence.The experimental results show that the proposed model achieves state-of-the-art performance on both Microsoft Research Asia(MSRA)corpus and people’s daily corpus,with F1 values of 94.77% and 95.97% respectively. | LI Daiyi TU Yaofeng ZHOU Xiangsheng ZHANG Yangming MA Zongmin | 2022 | ZTE Communications2022，20，S01： | 1 |
| 2 | Detection of common wound infection bacteria based on FAIMS technology显示文摘1 Introduction and main contributions In clinical surgery and postoperative rehabilitation, various types of wound infections can cause a variety of diseases, leading to further injury and even death of patients, which has become a common concern among doctors. The use of specific and rational antibiotics for patients is an important way to treat bacterial infections, while avoiding the abuse of antibiotics [1]. Therefore, rapid and reliable detection of infectious bacteria is the key premise of follow-up antibiotic treatment. | Shenyi QIAN Daiyi LI Tong SUN Bin YU | 2019 | Frontiers of Computer Science2019，13，4： | 1 |
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