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An Entity Relationship Extraction Model Based on BERT-BLSTM-CRF for Food Safety Domain

Dealing with food safety issues in time through online public opinion incidents can reduce the impact of incidents and protect human health effectively. Therefore, by the smart technology of extracting the entity relationship of public opinion events in the food field, the knowledge graph of the foo...

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Detalles Bibliográficos
Autores principales: Zhang, Qingchuan, Li, Menghan, Dong, Wei, Zuo, Min, Wei, Siwei, Song, Shaoyi, Ai, Dongmei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9071985/
https://www.ncbi.nlm.nih.gov/pubmed/35528358
http://dx.doi.org/10.1155/2022/7773259
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author Zhang, Qingchuan
Li, Menghan
Dong, Wei
Zuo, Min
Wei, Siwei
Song, Shaoyi
Ai, Dongmei
author_facet Zhang, Qingchuan
Li, Menghan
Dong, Wei
Zuo, Min
Wei, Siwei
Song, Shaoyi
Ai, Dongmei
author_sort Zhang, Qingchuan
collection PubMed
description Dealing with food safety issues in time through online public opinion incidents can reduce the impact of incidents and protect human health effectively. Therefore, by the smart technology of extracting the entity relationship of public opinion events in the food field, the knowledge graph of the food safety field is constructed to discover the relationship between food safety issues. To solve the problem of multi-entity relationships in food safety incident sentences for few-shot learning, this paper adopts the pipeline-type extraction method. Entity relationship is extracted from Bidirectional Encoder Representation from Transformers (BERTs) joined Bidirectional Long Short-Term Memory (BLSTM), namely, the BERT-BLSTM network model. Based on the entity relationship types extracted from the BERT-BLSTM model and the introduction of Chinese character features, an entity pair extraction model based on the BERT-BLSTM-conditional random field (CRF) is established. In this paper, several common deep neural network models are compared with the BERT-BLSTM-CRF model with a food public opinion events dataset. Experimental results show that the precision of the entity relationship extraction model based on BERT-BLSTM-CRF is 3.29%∼23.25% higher than that of other models in the food public opinion events dataset, which verifies the validity and rationality of the model proposed in this paper.
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spelling pubmed-90719852022-05-06 An Entity Relationship Extraction Model Based on BERT-BLSTM-CRF for Food Safety Domain Zhang, Qingchuan Li, Menghan Dong, Wei Zuo, Min Wei, Siwei Song, Shaoyi Ai, Dongmei Comput Intell Neurosci Research Article Dealing with food safety issues in time through online public opinion incidents can reduce the impact of incidents and protect human health effectively. Therefore, by the smart technology of extracting the entity relationship of public opinion events in the food field, the knowledge graph of the food safety field is constructed to discover the relationship between food safety issues. To solve the problem of multi-entity relationships in food safety incident sentences for few-shot learning, this paper adopts the pipeline-type extraction method. Entity relationship is extracted from Bidirectional Encoder Representation from Transformers (BERTs) joined Bidirectional Long Short-Term Memory (BLSTM), namely, the BERT-BLSTM network model. Based on the entity relationship types extracted from the BERT-BLSTM model and the introduction of Chinese character features, an entity pair extraction model based on the BERT-BLSTM-conditional random field (CRF) is established. In this paper, several common deep neural network models are compared with the BERT-BLSTM-CRF model with a food public opinion events dataset. Experimental results show that the precision of the entity relationship extraction model based on BERT-BLSTM-CRF is 3.29%∼23.25% higher than that of other models in the food public opinion events dataset, which verifies the validity and rationality of the model proposed in this paper. Hindawi 2022-04-28 /pmc/articles/PMC9071985/ /pubmed/35528358 http://dx.doi.org/10.1155/2022/7773259 Text en Copyright © 2022 Qingchuan Zhang et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Zhang, Qingchuan
Li, Menghan
Dong, Wei
Zuo, Min
Wei, Siwei
Song, Shaoyi
Ai, Dongmei
An Entity Relationship Extraction Model Based on BERT-BLSTM-CRF for Food Safety Domain
title An Entity Relationship Extraction Model Based on BERT-BLSTM-CRF for Food Safety Domain
title_full An Entity Relationship Extraction Model Based on BERT-BLSTM-CRF for Food Safety Domain
title_fullStr An Entity Relationship Extraction Model Based on BERT-BLSTM-CRF for Food Safety Domain
title_full_unstemmed An Entity Relationship Extraction Model Based on BERT-BLSTM-CRF for Food Safety Domain
title_short An Entity Relationship Extraction Model Based on BERT-BLSTM-CRF for Food Safety Domain
title_sort entity relationship extraction model based on bert-blstm-crf for food safety domain
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9071985/
https://www.ncbi.nlm.nih.gov/pubmed/35528358
http://dx.doi.org/10.1155/2022/7773259
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