## 5 Conclusions  
In this paper, a semi-supervised deep learning framework, GeoCWS, is proposed for CWS in the geoscience domain. First, a backbone model is suggested that combines a pseudo-lexicon with the BERT model to solve the problem of extracting complex geological word boundaries. By leveraging the pseudo-lexicon, which automatically updates based on the dataset, the model can more effectively handle domain-specific terminology. Next, a self-training strategy is put in place to enhance the generalization ability of the backbone with unlabeled data. This approach allows the model to generate high-quality pseudo-labels for the unlabeled data, thus enhancing its capacity to learn from a larger and more diverse dataset without requiring extensive manual annotation. Finally, the proposed method is compared with other benchmark methods on the geoscience domain dataset of the CWS task, and the experimental results show that the proposed method performs optimally.  
Although GeoCWS shows improvements over benchmark methods, there is still room for enhancement in predicting words with low label consistency and strong ambiguity. These issues arise due to the inherent complexities of geological terminology, where certain terms may have multiple meanings depending on context or may be inconsistently labeled across the dataset. These deficiencies highlight the need for further refinement in handling such terms and indicate areas where future improvements, such as domain-specific pretrained language models or better word-level information, could be implemented to increase the robustness and accuracy of the model in challenging scenarios. Additionally, Large Language Models (LLMs) have shown great potential in in-context learning, where a few task-specific examples allow the model to generate outputs for new inputs. We plan to leverage this capability to enhance data availability for CWS in the geoscience domain.  
Conceptualization, B.W.; methodology, Z.T.; formal analysis and investigation, Z.T.; writing\(-\)original draft preparation, Z.T.; writing\(-\)review and editing, B.W. and D.C.; funding acquisition, B.W.; supervision, Y.D., F.F. and Y.W. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Key Research and Development Program of China (No.2016 YFB0502300) and the Geological Survey Project (No.12120114074001).  
**Institutional Review Board Statement:** Not applicable.  
Figure 7: Examples of error type 1 and type 2.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data and codes that support the findings of this study are available at [[https://figshare.com/s/289d7](https://figshare.com/s/289d7) da6f65 dc3c9 ddc7]([https://figshare.com/s/289d7](https://figshare.com/s/289d7) da6f65 dc3c9 ddc7) (accessed on 25 January 2025).  
**Acknowledgments:** Thanks to all reviewers for their careful reading and insightful comments on this article. We extend our sincere appreciation to [PERSON], [PERSON], and [PERSON] for their assistance and support throughout the research process.  
**Conflicts of Interest:** The authors declare no conflicts of interest.