# Chinese Named Entity Recognition of Geological News Based on BERT Model
## 4 Conclusions  
This study uses a deep learning-based model to perform named entity recognition on geological news texts. As there is no public labeling training dataset for model comparison in the field of geological news, we collected geological news texts from the China Geological Survey. We annotated the data using automatic annotation and manual calibration with the open source annotation tool YEDDA to build a corpus of geological news texts. In past NER research in the geological field, [PERSON] et al. [23] proposed the BERT- BiLSTM-CRF model to perform entity recognition in Chinese mineral texts. In the experiment, eight entity types were extracted, and their F1 scores all exceeded 95%. [PERSON] et al. [24] obtained F1 scores of 94.65% and 95.67% on the MSRA and People's Daily corpora, respectively, in the BERT-BiLSTM-CRF model for named entities. Our experiment compares six models on the constructed geological news dataset. The F1 score of the BERT-BiLSTM-CRF model is 0.838, which achieves a high entity recognition effect, and the F1 score of the BiLSTM-CRF model is 0.768, which is less efficient than the BERT-based models. Compared to the previous study, we annotated 21,323 entities, trained based on the geological news dataset, extracted six entity types, and the F1 score of the model reached 0.839. The experimental results show that the proposed model can also achieve a good entity recognition effect in the field of geological news.  
Although this research has achieved good results in NER tasks in the field of geological news, there are still some shortcomings and areas that can be further improved. First, the geological news corpus constructed in this study is relatively small, resulting in too few job titles and event entities, thus influencing the effect of entity recognition. Therefore, the dataset should be expanded in future studies. Second, as the process is cumbersome, it is inevitable that some labeling errors will occur. Moreover, the original model should be improved to enhance its performance and improve the entities' recognition effect. The information extracted from the geological news text can be applied to constructing geological news knowledge graphs.  
Figure 6: Changes in F1 score with increasing epochs.  
**Author Contributions:** Conceptualization, Y.W.; methodology, C.H. and Y.W.; software, C.H.; validation, Y.H., Y.L. and X.Z.; data curation, C.H. and Y.Y.; writing--original draft preparation, C.H.; writing--review and editing, Y.W., Y.Y. and Y.H.; supervision, Y.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the China University of Geosciences (Beijing) College Students' Innovation and Entrepreneurship Training Program under Grant X202211415100.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data sharing is not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study, in the collection, analyses, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.