A Chinese Nested Named Entity Recognition Model for Chicken Disease Based on Multiple Fine-Grained Feature Fusion and Efficient Global Pointer

Extracting entities from large volumes of chicken epidemic texts is crucial for knowledge sharing, integration, and application. However, named entity recognition (NER) encounters significant challenges in this domain, particularly due to the prevalence of nested entities and domain-specific named e...

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Principais autores:	Xiajun Wang, Cheng Peng, Qifeng Li, Qinyang Yu, Liqun Lin, Pingping Li, Ronghua Gao, Wenbiao Wu, Ruixiang Jiang, Ligen Yu, Luyu Ding, Lei Zhu
Format:	Artigo
Jezik:	Inglês
Izdano:	MDPI AG 2024-09-01
Serija:	Applied Sciences
Teme:	nested named entity recognition chicken disease multiple fine-grained feature fusion RoBERTa efficient global pointer
Online dostop:	https://www.mdpi.com/2076-3417/14/18/8495
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