###### Abstract  
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 entities, coupled with a scarcity of labeled data. To address these challenges, we compiled a corpus from 50 books on chicken diseases, covering 28 different disease types. Utilizing this corpus, we constructed the CDNER dataset and developed a nested NER model, MFGFF-BiLSTM-EGP. This model integrates the multiple fine-grained feature fusion (MFGFF) module with a BiLSTM neural network and employs an efficient global pointer (EGP) to predict the entity location encoding. In the MFGFF module, we designed three encoders: the character encoder, word encoder, and sentence encoder. This design effectively captured fine-grained features and improved the recognition accuracy of nested entities. Experimental results showed that the model performed robustly, with F1 scores of 91.98%, 73.32%, and 82.54% on the CDNER, CMeEE V2, and CLUENER datasets, respectively, outperforming other commonly used NER models. Specifically, on the CDNER dataset, the model achieved an F1 score of 79.68% for nested entity recognition. This research not only advances the development of a knowledge graph and intelligent question-answering system for chicken diseases, but also provides a viable solution for extracting disease information that can be applied to other livestock species. nested named entity recognition; chicken disease; multiple fine-grained feature fusion; RoBERTa; efficient global pointer +
Footnote †: journal: _Sci._  
_Applied_  
_Acticle_  
A Chinese Nested Named Entity Recognition Model for Chicken Disease Based on Multiple Fine-Grained Feature Fusion and Efficient Global Pointer  
[PERSON]  
1,2,2,3,4,5,6, [EMAIL_ADDRESS]. has heightened the threat of various poultry diseases, which can severely impact production and livelihoods. As a result, effective disease prevention and control have become increasingly critical. Despite the availability of information on poultry diseases, it is often fragmented and poorly organized, making it difficult for farmers to access the necessary knowledge when needed. This paper addresses this issue by proposing a method that utilizes deep learning techniques to accurately identify and extract essential information on poultry diseases from extensive text sources. This approach lays the foundation for developing a comprehensive knowledge graph, which can support advanced applications such as intelligent Q&A systems and efficient knowledge retrieval platforms [1]. These tools will equip farmers with the information they need to protect their flocks and maintain their livelihoods. Named entity recognition (NER) is a critical task in natural language processing that involves the identification of specific entities within text. Its importance has grown significantly with the rapid expansion in the biomedical literature and data. In the biomedical domain, biomedical named entity recognition (BioNER) targets the recognition of entities such as disease names, symptoms, drugs, and anatomical parts [2]. Traditionally, NER models have approached this task as a sequence labeling problem, employing both rule-based and machine learning techniques. Rule-based methods necessitate the manual creation of extensive rule sets that rely heavily on domain expertise and are often restricted to narrowly defined areas [3]. In contrast, machine learning approaches including hidden Markov models (HMMs), maximum entropy models (MEMs), support vector machines (SVMs), and conditional random fields (CRFs) [4; 5; 6; 7] rely on feature engineering. This reliance poses challenges, particularly in selecting appropriate features and capturing long-term dependencies between entities [8].