融合对抗训练与胶囊网络的羊疾病命名实体自动标注方法OA

Disease prevention and control are crucial for the healthy development of the sheep industry.However,weak capabilities of the primary diagnosis and over-reliance on expert experience often lead to delay in disease management.Deep learning-based large models offer an innovative solution for assisted diagnosis,for which sheep disease texts serve as core carriers of knowledge in this field.Named Entity Recognition(NER)is a key step towards automatic text annotation.Nevertheless,sheep disease corpora faces challenges such as imbalanced entity distribution,complex semantic relationships,and ambiguous entity boundaries.To address these issues,this study analyzed the characteristics of sheep disease texts,established annotation rules,and constructed a corpus containing 9 categories and 9,635 entities to support NER tasks,proposing a named entity recognition model for sheep disease texts—SD-ATCN(A Named Entity Recognition Model for Sheep Diseases Texts Integrating Adversarial Training and Capsule Networks).First,the RoBERTa model was used to extract contextual embeddings,which were integrated with adversarial samples generated through adversarial training to form the output of the text embedding layer.This enhanced the model's stability in handling noisy data and imbalanced annotation scenarios.Then,capsule networks were incorporated into the global features extracted by BiLSTM to improve the model's ability to capture complex entity relationships.Finally,a CRF layer decoded the entity labels to achieve automatic entity recognition.Additionally,differential learning rates were applied during training to flexibly adjust learning rates across different layers,accelerating convergence and improving performance.Experimental results showed that the model respectively achieved precision,recall,and F,scores as 93.28％,95.57％,and 94.40％,outperforming the baseline RoBERTa-BiLSTM-CRF model by 2.13％,1.20％,and 1.69％.Based on the SD-ATCN model,an automatic annotation system for sheep disease texts was developed,significantly improving annotation efficiency and providing technical support for automated text processing and intelligent diagnosis in sheep disease management.