# Enhanced Chinese Domain Named Entity Recognition:
## 4 Experiments
### Results and Analysis
#### 4.2.4 Comparative with Various Strategies  
In order to investigate the contribution of each strategy to our approach, we conducted a series of ablation experiments on the SubDuIE and PPOST datasets, and the results are shown in Figure 9.  
The ablation studies were designed as follows:  
* Strategy 1: Strategy of adding only domain term frequency weight features without additional methods for feature fusion;  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Parameters** & **Speed** \\ \hline BERT-GlobalPointer & 29.00 M & 1\(\times\) * \\ BERT-LPN & 28.97 M & 1.15\(\times\) * \\ BERT-Pointer & 28.94 M & 1.17\(\times\) * \\ \hline \hline \end{tabular}  
* 1.15\(\times\) and 1.17\(\times\) represent 15% and 17% faster than 1\(\times\), respectively.  
\end{table}
Table 6: Parameters and training speed statistics of the GlobalPointer, LPN, and Pointer models based on BERT on the PPOST dataset.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Long Entities**} & \multicolumn{3}{c}{**Nested Entities**} \\ \cline{2-7}
**Model** & **P(\%)** & **R(\%)** & **F1(\%)** & **P(\%)** & **R(\%)** & **F1(\%)** \\ \hline BiLSTM-CRF & 43.36 & 39.76 & 41.48 & 47.68 & 40.01 & 43.50 \\ BERT & 46.90 & 41.47 & 44.02 & 45.19 & 37.31 & 40.87 \\ BERT-CRF & 48.43 & 41.29 & 44.58 & 42.17 & 35.31 & 38.44 \\ BERT-BiLSTM-CRF & 49.00 & 40.39 & 44.28 & 43.16 & 37.99 & 40.41 \\ Lattice LSTM & 51.49 & 45.62 & 48.38 & 44.11 & 41.01 & 42.50 \\ LEBERT & 52.04 & 45.84 & 48.74 & 45.80 & 42.61 & 44.15 \\ BERT-GlobalPointer & 57.42 & 53.73 & 55.51 & 65.80 & 56.76 & 60.95 \\ BERT-Pointer & 59.00 & 54.71 & **56.77** & 67.67 & 58.30 & **62.64** \\ BERT-LPN & 60.39 & 55.19 & **57.67** & 68.32 & 59.26 & **63.47** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Precision, recall, and F1-score statistics of long entities and nested entities on the PPOST.  
* Strategy 2: Strategy of adding only Chinese lexicon boundary features without additional methods for feature fusion;
* Strategy 3: Strategy of simultaneously adding Chinese lexicon boundary and domain term frequency weight features without additional methods for feature fusion;
* Strategy 4: Strategy of simultaneously adding Chinese lexicon boundary and domain term frequency weight features, using the BiLSTM for feature fusion.  
We remove any strategy that will lead to a decrease in entity recognition accuracy. Specifically, compared to the original pointer network, Strategy 1, Strategy 2, and Strategy 3 have all shown improvements on our experimental SubDulE and PPOST datasets. Their F1-scores increased by 0.28%, 0.54%, 0.67% and 0.67%, 0.72%, 1.84%, respectively. This indicates that lexicon boundary features and domain term frequency weight features can effectively integrate Chinese lexicon-level features, improving the performance of the NER model. Compared to Strategy 3, Strategy 4 and the Complete model showed F1-score improvements of 0.13% and 0.44% and 0.18% and 0.44%, respectively, on the above two datasets. This suggests that the feature fusion of Chinese lexicon-level feature vectors can more effectively integrate contextual information features, enhancing the performance of the NER model. Compared to Strategy 4, the Complete model showed F1-score improvements of 0.31% and 0.26% on the above two datasets, respectively, indicating that the multi-head self-attention mechanism better handles dependencies between information.