## 2 Materials and Methods
### MGFGF-BiLSTM-EGP
This superior performance can be attributed to RoBERTa's use of a larger dataset, extended training time, and the removal of the next sentence prediction task from BERT. These modifications enhanced RoBERTa's ability to capture contextual information and manage long dependencies effectively. MCBERT, a model pre-trained specifically for the Chinese medical domain, performed comparably to BERT in medical text classification tasks, with accuracy and F1 scores of 90.25% and 90.49%, respectively. MCBERT's strength lies in its pre-training on a vast corpus of medical domain texts, enabling it to handle medical terminology and specialized sentence structures effectively. However, compared to RoBERTa and ERNIE, MCBERT was slightly weaker in precision and recall, likely due to the more extensive pre-training data and optimization strategies employed by RoBERTa and ERNIE, which resulted in better performance even in specialized areas like chicken disease NER. XLNet, on the other hand, lagged behind the other models in precision (88.87%), recall (90.08%), and F1 score (89.44%). Despite its au

Figure 11: Comparison of the evaluation results of 5 pre-trained models under the MFGFF-BiLSTM-EGP model. toregressive architecture, which aims to merge the benefits of BERT and Transformer-XL to capture richer contextual information, XLNet's performance may degrade when handling shorter texts or when there is insufficient contextual information. ## 5 Conclusions

In this study, we constructed the CDNER dataset, specifically designed for nested named entity recognition (NER) in the context of chicken diseases. Additionally, we developed a novel model, termed MFGFF-BiLSTM-EGP. This model achieved strong results across multiple datasets, with F1 scores of 91.98%, 73.32%, and 82.54% on the CDNER, CMeEE V2, and CLUENER datasets, respectively, highlighting its broad generalization capabilities. Notably, the use of a character encoder contributed the most significant improvement (9.31%), while the sentence encoder offered a modest boost (0.65%). The MFGFF module further enhanced the model's performance by integrating fine-grained feature vectors. The model excelled at recognizing nested entities, with F1 scores of 75.95% on the CDNER dataset and 44.50% on the CMeEE V2 dataset, reflecting improvements of 4.04% and 4.18% compared to using word vector embeddings. This validates the effectiveness of the EGP module in handling nested structures. Despite achieving promising results, challenges remain. The model exhibited suboptimal performance in distinguishing closely related entities, with instances of misclassification and missed classifications highlighting limitations in semantic understanding and contextual processing. Additionally, the feature fusion process may introduce information redundancy. Future work will focus on addressing issues of misclassification and missed classifications, optimizing feature selection, reducing information redundancy, and enhancing the overall model performance. This research not only advances the theoretical application of deep learning to complex NER tasks, particularly for nested entity recognition in animal disease contexts, but also offers practical benefits. In the field of agriculture and animal health, the model could be integrated into intelligent diagnostic tools, enabling veterinarians and farmers to develop disease management plans quickly and accurately. Additionally, it lays the foundation for constructing animal disease knowledge graphs, which can be applied in question-answer systems to boost decision-making efficiency. Not applicable. The original data presented in the study are openly available in [[https://github.com/wangxiajun68/chicken-disease](https://github.com/wangxiajun68/chicken-disease)]([https://github.com/wangxiajun68/chicken-disease](https://github.com/wangxiajun68/chicken-disease)) (accessed on 18 September 2024). The authors declare no conflicts of interest. ## References

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