A joint model for entity and relation extraction based on BERT

Qiao, Bo; Zou, Zhuoyang; Huang, Yu; Fang, Kui; Zhu, Xinghui; Chen, Yiming

doi:10.1007/s00521-021-05815-z

Cited by 62 publications

(27 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Gao et al [ 24 ] proposed a medical relationship extraction model based on BERT, which combined the whole sentence information obtained from the pretrained language model with the corresponding information of two medical entities to complete the relationship extraction task. Qiao et al [ 25 ] proposed an agricultural entity relationship extraction model based on BERT-BLSTM-LSTM, which can effectively extract the relationship between agricultural entities.…”

Section: Related Workmentioning

confidence: 99%

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

Zhang

Dong

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%

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

Zhang

Dong

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Based on the relationship extraction method of the feature vector, first build a feature vector for the original text, and then find the relationship between the entities through the statistical learning model [ 9 ]. Qiao proposed a relationship extraction method based on the maximum entropy model, which is integrated with the lexical, syntax, and semantic feature vectors [ 10 ]. Pagan added the word block information and Word-net information to the built-in feature vector and enhanced the text's syntax information and semantic information [ 11 ].…”

Section: Related Discussionmentioning

confidence: 99%

Construction and Application of Text Entity Relation Joint Extraction Model Based on Multi-Head Attention Neural Network

Xue

Zhu

Lyu

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Entity relationship extraction is one of the key areas of information extraction and is an important research content in the field of natural language processing. Based on past research, this paper proposes a combined extraction model based on a multi-headed attention neural network. Based on the BERT training model architecture, this paper extracts textual entities and relations tasks. At the same time, it integrates the naming entity feature, the terminology labeling characteristics, and the training relationship. The multi-attention mechanism and improved neural structures are added to the model to enhance the characteristic extraction capacity of the model. By studying the parameters of the multi-head attention mechanism, it is shown that the optimal parameters of the multi-head attention are h = 8, dv = 16, and the classification effect of the model is the best at this time. After experimental analysis, comparing the traditional text entity relationship extraction model and the multi-head attention neural network joint extraction model, the model entity relationship extraction effect was evaluated from the aspects of comprehensive evaluation index F1, accuracy rate P, and system time consumed. Experiments show: First, in the accuracy indicator, Xception performance is best, reaching 87.7%, indicating that the model extraction feature effect is enhanced. Second, with the increase of the number of iterative times, the verification set curve and the training set curve have increased to 96% and 98%, respectively, and the model has a strong generalization ability. Third, the model completes the extraction of all data in the test set in 1005 ms, which is an acceptable speed. Therefore, the model test results in this article are good, with a strong practical value.

show abstract

“…Bert is a huge pre-trained model that shows excellent performance when generating entity embeddings of text (Qiao, 2021). The Bert model improves the performance of text classification through entity embedding, so it can identify tweets and generate corresponding damage reports, finally we classify the damage reports into corresponding topics.…”

Section: Text Data Classification Modelmentioning

confidence: 99%

A multimodal data analysis approach for social media during natural disasters

zhang

huang

2022

Preprint

View full text Add to dashboard Cite

During natural disaster, social media can provide real-time or low delayed disaster perception information to help government managers carry out disaster response efforts efficiently,therefore, it is of great significance to mining social media information accurately. Distinguished from previous studies, this study proposed a multi-modal data classification model for mining social media information. This model conducted LDA(Late Dirichlet Allocation) to identify subject information from multi-modal data and then analyzed by Bert (bidirectional encoder representation from transformers) and Vgg-16 (visual geometry group 16), text data and image data were classified separately, resulting in real mining of topic information during disasters. This paper used Weibo data during the 2021 Henan heavy storm as the research object, comparing with previous data experiment results, this study proposed a model that can make natural disaster related topic classification of social media data more accurately, resulting in a real-time understanding of different themed natural disasters, it was helpful in making disastrous decisions.

show abstract

A joint model for entity and relation extraction based on BERT

Cited by 62 publications

References 22 publications

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

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

Construction and Application of Text Entity Relation Joint Extraction Model Based on Multi-Head Attention Neural Network

A multimodal data analysis approach for social media during natural disasters

Contact Info

Product

Resources

About