## 5 Conclusions  
High-quality extraction of entities related to pig diseases is critical for intelligent consultation, question answering, technical recommendations, and other application scenarios.  
In this study, we constructed a corpus, labeled datasets, and lexicon for Chinese named entity recognition specific to pig diseases, encompassing 152,596 characters, 7518 entities, and 2391 professional terms. To tackle the challenges of entity identification in the pig disease domain, such as the scarcity of annotated data, numerous technical terms, and fuzzy boundaries, we propose the PDCNER model. This model integrates lexicon information from the pig disease domain into BERT's Transformer layers at the lower level and employs contrastive learning to enhance representation quality and generalization capability. The results indicate that the PDCNER model surpasses the performance of BERT-BiLSTM-CRF and other mainstream models, achieving precision, recall, and F1-score of 87.76%, 86.97%, and 87.36%, respectively. This demonstrates high-quality entity recognition in  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Model** & \multicolumn{3}{c}{**Weibo**} & \multicolumn{3}{c}{**Ontonotes**} & \multicolumn{3}{c}{**Resume**} \\ \hline  & **P** & **R** & **F1** & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline BERT-BiLSTM-CRF & 71.29 & 67.1 & 69.13 & 84.11 & 80.2 & 82.11 & 96.56 & 97.23 & 95.89 \\ \hline LEBERT & 78.2 & 74.64 & 76.38 & 89.76 & 82.68 & 86.07 & 95.89 & 97.42 & 96.65 \\ \hline
**PDCNER (ours)** & 81.42 & 76.56 & **78.91** & 89.68 & 83.43 & **86.44** & 96.07 & 97.36 & **96.71** \\ \hline \hline \end{tabular}
\end{table}
Table 5: F1-score for each model on public datasets.  
the field of pig diseases. Moreover, small-sample experiments confirm that our model is more suitable than other models for completing the named entity recognition task in data-scarce scenarios. Experiments on public datasets also verify its generalization ability. Our approach provides a reference for improving NER performance in domain-specific applications. It can be easily applied to other fields, facilitating seamless adaptation for named entity identification across diverse contexts.  
In future work, we plan to focus on the following aspects:  
1. We aim to enhance the identification of more fine-grained entity types in the pig disease domain, including appearance symptoms and anatomical symptoms.
2. We will further optimize the pig disease corpus and construct a more extensive professional vocabulary to enhance NER performance.
3. We will explore the application of our model in other animal diseases, such as chicken or cow diseases, and investigate methods to handle nested entities and discontinuous entities based on our approach.  
Conceptualization, C.P. and Q.L.; Data curation, R.M., H.L., S.W. and L.H.; Formal analysis, W.W. and H.H.; Investigation, S.W.; Methodology, C.P. and X.W.; Software, X.W.; Validation, Q.Y., R.J. and W.M.; Writing \(-\) original draft, C.P. and X.W.; Writing \(-\) review and editing, C.P. and Q.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Science and Technology Major Project (2021 ZD0113802).  
Not applicable.  
**Data Availability Statement:** As the datasets used in this manuscript will be used for other technical research, they are available from the corresponding author upon reasonable request. The codes have been open-sourced at [[https://github.com/tuteifei923/pdcner](https://github.com/tuteifei923/pdcner)]([https://github.com/tuteifei923/pdcner](https://github.com/tuteifei923/pdcner)) (accessed on 5 August 2024).  
We thank the editors and the anonymous reviewers for their valuable suggestions.  
The authors declare no conflicts of interest.