# Chinese Named Entity Recognition of Geological News Based on BERT Model
## 3 Results
### Comparison of Different Models  
We divided the labeled dataset into the training set, validation set, and test set according to a ratio of 8:1:1, with 17,124, 2056, and 2143 entities, respectively. In the NER task of the geological news dataset, we trained the dataset on six different models and tested it on the test set. The experimental results show that the model has achieved good results in GNNER. Table 4 presents relevant information on the precision rate, recall rate, and F1 scores of the six models for six categories of entities: name, time, geographic location, job title, organization, and event. By analyzing the above experimental results, we can draw the following conclusions:  
1. The six models adopted in the experiment have achieved good results in the geological news text NER task. 2. In the geological news text NER task, the F1 scores of the BERT, DistilBERT, RoBERTa, BERT-CRF, and BERT-BiLSTM-CRF models are 0.824, 0.814, 0.828, 0.839, and 0.838, respectively. Compared to BiLSTM-CRF, the F1 scores increase by 5.6%, 4.6%, 6%, 7.1%, and 7%. This shows that as the BERT pre-training model can understand the contextual information of the text well and solve the polysemy problem, it has a good effect on the named entity recognition task. 3. The improved DistilBERT and RoBERTa models based on BERT achieve F1 scores of 0.814 and 0.828, respectively. Compared to the BERT model, the entity recognition effect of the DistilBERT model is slightly worse, while the RoBERTa model is better. 4. In the geological news text NER task, the P, R, and F1 scores of the BERT-CRF model improve by 1.9%, 1.2%, and 1.5%, respectively, compared to the BERT model because of a mutual constraint relationship between the tags (e.g., the tag of an entity can only start with \"B\" but not \"I\"). It can be seen after adding the CRF layer that CRF can \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Model** & **Eval** & **TIM** & **ORG** & **POS** & **EVE** & **LOC** & **PER** & **Avg** \\ \hline \multirow{4}{*}{BERT} & P & 0.863 & 0.820 & 0.691 & 0.820 & 0.802 & 0.887 & 0.819 \\  & R & 0.827 & 0.844 & 0.827 & 0.796 & 0.835 & 0.806 & 0.829 \\  & F & 0.845 & 0.832 & 0.753 & 0.808 & 0.818 & 0.844 & 0.824 \\ \hline \multirow{4}{*}{DistilBERT} & P & 0.847 & 0.796 & 0.721 & 0.793 & 0.787 & 0.864 & 0.808 \\  & R & 0.804 & 0.852 & 0.803 & 0.812 & 0.824 & 0.816 & 0.821 \\  & F & 0.825 & 0.823 & 0.760 & 0.802 & 0.805 & 0.839 & 0.814 \\ \hline \multirow{4}{*}{RoBERTa} & P & 0.865 & 0.828 & 0.725 & 0.815 & 0.808 & 0.876 & 0.823 \\  & R & 0.823 & 0.841 & 0.814 & 0.821 & 0.827 & 0.829 & 0.834 \\  & F & 0.843 & 0.834 & 0.767 & 0.818 & 0.817 & 0.852 & 0.828 \\ \hline \multirow{4}{*}{BiLSTM-CRF} & P & 0.893 & 0.876 & 0.864 & 0.563 & 0.812 & 0.854 & 0.814 \\  & R & 0.765 & 0.803 & 0.760 & 0.697 & 0.687 & 0.616 & 0.728 \\  & F & 0.824 & 0.838 & 0.809 & 0.623 & 0.744 & 0.716 & 0.768 \\ \hline \multirow{4}{*}{BERT-CRF} & P & 0.841 & 0.837 & 0.733 & 0.803 & 0.849 & 0.878 & 0.838 \\  & R & 0.860 & 0.847 & 0.811 & 0.808 & 0.841 & 0.840 & **0.841** \\  & F & 0.850 & 0.842 & 0.770 & 0.805 & 0.845 & 0.859 & **0.839** \\ \hline \multirow{4}{*}{BERT-BiLSTM-CRF} & P & 0.844 & 0.844 & 0.739 & 0.853 & 0.843 & 0.827 & **0.839** \\  & R & 0.835 & 0.846 & 0.863 & 0.838 & 0.838 & 0.811 & 0.838 \\ \cline{1-1}  & F & 0.839 & 0.845 & 0.796 & 0.846 & 0.840 & 0.819 & 0.838 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: P, R, and F1 scores of six types of entities on six models. The numbers in bold font are the three indicators (P, R, F1) with the highest corresponding scores in the experiment. deal with the mutual constraint relationship between the tags and effectively solve the problem of inconsistent sequence tags. 5. The P, R, and F1 scores of BERT-CRF are 0.838, 0.841, and 0.839, respectively, which are the best among all models. Compared to the BERT-BiLSTM-CRF model, which introduced the BiLSTM layer, the two achieved comparable results in the NER task of geological news texts.