A method for extracting aquatic animal disease prevention and control events integrated with capsule network DOI Creative Commons

Mingyang Sha,

Sijia Zhang,

Qingcai Fu

et al.

Deleted Journal, Journal Year: 2024, Volume and Issue: 6(7)

Published: June 21, 2024

Abstract Addressing the issue of long-tail event entity recognition in aquatic animal disease prevention and control, this paper proposes an extraction method that integrates capsule networks. The designs two parallel networks: first utilizes BERT + TextCNN to extract initial local features from text, while Multi-BiLSTM further captures multi-dimensional dependency information features. second network employs networks learns spatial semantic relationships among different entities. extracted both are then fused. Experimental results demonstrate achieves significant performance on control dataset, with F1 score 75.83%, effectively addressing challenge recognition.

Language: Английский

Base on ChatGLM extraction of medication events in aquaculture with few samples DOI
Zhenglin Li, Sijia Zhang,

Zongshi An

et al.

Aquaculture International, Journal Year: 2025, Volume and Issue: 33(1)

Published: Jan. 1, 2025

Language: Английский

Citations

1
A Two-Stage Boundary-Enhanced contrastive learning approach for nested named entity recognition DOI

Yaodi Liu,

Kun Zhang, Rong Tong

et al.

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 126707 - 126707

Published: Jan. 1, 2025

Language: Английский

Citations

1
Research on fine-tuning strategies for text classification in the aquaculture domain by combining deep learning and large language models DOI
Zhenglin Li, Sijia Zhang,

Peirong Cao

et al.

Aquaculture International, Journal Year: 2025, Volume and Issue: 33(4)

Published: April 29, 2025

Language: Английский

Citations

0
A method for extracting aquatic animal disease prevention and control events integrated with capsule network DOI Creative Commons

Mingyang Sha,

Sijia Zhang,

Qingcai Fu

et al.

Deleted Journal, Journal Year: 2024, Volume and Issue: 6(7)

Published: June 21, 2024

Abstract Addressing the issue of long-tail event entity recognition in aquatic animal disease prevention and control, this paper proposes an extraction method that integrates capsule networks. The designs two parallel networks: first utilizes BERT + TextCNN to extract initial local features from text, while Multi-BiLSTM further captures multi-dimensional dependency information features. second network employs networks learns spatial semantic relationships among different entities. extracted both are then fused. Experimental results demonstrate achieves significant performance on control dataset, with F1 score 75.83%, effectively addressing challenge recognition.

Language: Английский

Citations

1