# Chinese Fine-Grained Named Entity Recognition Based on BILTAR and GlobalPointer Modules
## 4 Experiment
### Comparative Experiment  
#### 4.4.1 Baseline Models  
To prove the effectiveness of the model, comparison experiments were conducted to compare the proposed model with baseline models and classic entity recognition neural network models proposed in recent years. The comparison results are shown in Tables 3 and 4. Bold data indicates the highest value of the corresponding indicator in the table.  
BERT-CRF encodes the semantic features of the text with the BERT model and applies the CRF layer for sequence decoding to obtain the prediction results. BERT-GlobalPointer uses rotation position encoding (RoPE) on the output features of the BERT model to add position information and extracts entities by calculating the score of the entity boundary in the diagonal matrix. AT-CBGP [34] improves the robustness and generalization of the model by adding an adversarial neural network on the GlobalPointer module. L  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Compare Models** & **P\%** & **R\%** & **F1\%** \\ \hline BERT-ATT-BILTAR-GLOBALPOINTER & **79.227** & **72.720** & **75.751** \\ BERT-GLOBALPOINTER & 71.691 & 67.838 & 69.644 \\ BERT-CRF & 62.831 & 69.607 & 66.046 \\ LLPA & 57.440 & 67.010 & 61.860 \\ MFT & 63.720 & 65.030 & 64.380 \\ BERT + para-lattice + CRF & 68.920 & 69.740 & 70.570 \\ AT-CBGP & 74.110 & 68.700 & 71.190 \\ BERT + BILSTM + CRF & 55.701 & 62.254 & 58.796 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Contrast experiments of Weibo dataset.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline BILSTM hidden size & 128*d\_classes \\ Number of BILSTM layers & 1 \\ Dropout rate & 0.5 \\ Optimizer & Adam \\ Learning rate & \(2\times 10^{-5}\) \\ Epoch & 20 (CLUENER2020)/50 (Weibo) \\ Batch size & 64 \\ Bert output dimension & 768 \\ \multirow{2}{*}{Learning rate adjustment strategy} & \multirow{2}{*}{
\begin{tabular}{c} CosineAnnealingWarmRestarts \\ (CLUENER2020)/MultiStepLR (Weibo) \\ Bert-base-chinese \\ \end{tabular} } \\ \cline{1-1} \cline{2-3} \cl  
relative position encoding on the bidirectional Lattice-LSTM module. MFT [36] adds Chinese word root information and improves the structure of the transformer. BERT + para-lattice + CRF [37] adds Chinese character vectors as features to enhance performance.  
This paper proves the effectiveness of the proposed model on the CLUENER2020 dataset by comparing its performance with that of classic models in comparative experiments. The following models are classic entity recognition network structures that were reimplemented in this paper. BERT-CRF extracts the semantic information of the text with the BERT layer and learns the constraint information between entity tags with the CRF conditional random field. On the basis of the BERT-CRF module, three complex entity recognition models are created by adding the feature extraction layer IDCNN module, the BILSTM module, and the IDCNN-BILSTM module, respectively, to obtain four classic entity recognition models.  
The experimental results of AT-CBGP, LLPA, MFT, and BERT + para-lattice + CRF are the experimental results of other papers, and the remaining six baseline models are the experimental results obtained by reproducing other papers' models. The Weibo dataset is divided into training, testing, and validation sets. The training method on the Weibo dataset is to verify the validation set after each round of training, and after 50 rounds of training, the model selects the model parameters with the highest performance on the validation set, tests the performance on the test set with the best model parameters, and the experimental results on the test set are the final results of the model. The CLUENER 2020 dataset is divided into training and validation sets. The training method on the CLUENER2020 dataset is to validate the validation set after each round of training, and after 20 rounds of training, the model selects the best performance on the validation set as the final result of the model.