Chinese Named Entity Recognition of Epidemiological Investigation of Information on COVID-19 Based on BERT
Ieee Access
; 10:104156-104168, 2022.
Article
in English
| Web of Science | ID: covidwho-2070271
ABSTRACT
The named entity recognition based on the epidemiological investigation of information on COVID-19 can help analyze the source and route of transmission of the epidemic to control the spread of the epidemic better. Therefore, this paper proposes a Chinese named entity recognition model BERT-BiLSTM-IDCNN-ELU-CRF (BBIEC) based on the epidemiological investigation of information on COVID-19 of the BERT pre-training model. The model first processes the unlabeled epidemiological investigation of information on COVID-19 into the character-level corpus and annotates it with artificial entities according to the BIOES character-level labeling system and then uses the BERT pre-training model to obtain the word vector with position information;then, through the bidirectional long-short term memory neural network (BiLSTM) and the improved iterated dilated convolutional neural network (IDCNN) extract global context and local features from the generated word vectors and concatenate them serially;output all possible label sequences to the conditional random field (CRF);finally pass the condition random The airport decodes and generates the entity tag sequence. The experimental results show that the model is better than other traditional models in recognizing the entity of the epidemiological investigation of information on COVID-19.
COVID-19; Feature extraction; Bit error rate; Hidden Markov models; Biological system modeling; Encoding; Text recognition; Long short term; memory; Chinese named entity recognition; the epidemiological; investigation of information on COVID-19; bidirectional encoder; representations from transformer; bidirectional long-short term memory; network; iterated dilated convolutional neural network; conditional; random field
Full text:
Available
Collection:
Databases of international organizations
Database:
Web of Science
Type of study:
Observational study
Language:
English
Journal:
Ieee Access
Year:
2022
Document Type:
Article
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