# A Local Information Perception Enhancement-Based Method for Chinese NER
## 4 Experimental Results and Comparison
### Overall Results  
We compare the recently widely used lexicon-based character enhancement model and the Chinese NER method, applying graph neural networks as baseline methods with our proposed methods. Comparisons on the Weibo, Resume, and OntoNotes datasets are shown in Table 3.  
Table 3 shows the experimental results of NER obtained by our model and other baseline models on the Weibo dataset. We compare our model with three traditional lexicon-based NER models, Lattice-LSTM [7], LR-CNN [8], and PLTE [38], and two graph neural network-based models, LGN [25] and Multi-Graph [37]. In addition, three recent mainstream lexicon-based models are also compared with our model. We can observe that the SoftLexicon-BERT [13] model achieves the best F1 score of 70.63% among all baseline models. On the other hand, our model improves the total F1 score to 71.81%, an increase of 1.18%. The results of the Resume dataset are shown in Table 3. The F1 score of Lattice-LSTM [7], which released the dataset, is 94.46%; the F1 score of LR-CNN [8] is 95.11%; and the F1 score of the improved PLTE [38] is 95.40%. In addition, PGAT-BERT [27] uses the polymorphic graph attention network based on SoftLexicon [13], which obtained the \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Hyper** & **Weibo** & **Resume** & **OntoNotes** & **E-Commerce** \\ \hline Batch Size & 8 & 8 & 8 & 1 \\ Decay Rate & 0.05 & 0.05 & 0.05 & 0.05 \\ Learning Rate & 0.005 & 0.003 & 0.003 & 0.003 \\ Hidden Size & 200 & 200 & 300 & 200 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The hyperparameters for best models that we have experimented on the given datasets. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **Weibo** & **Resume** & **OntoNotes** \\ \hline Lattice-LSTM [7] & 58.79 & 94.46 & 73.88 \\ LR-CNN [8] & 59.92 & 95.11 & 74.45 \\ PLTE [38] & 59.92 & 95.40 & 74.60 \\ LGN [25] & 60.15 & 95.41 & 74.85 \\ Multi-Graph [37] & 59.50 & - & 76.00 \\ \hline BERT-LSTM-CRF & 67.33 & 95.51 & 81.82 \\ FLAT-BERT [9] & 68.55 & 95.86 & 81.82 \\ SoftLexicon-BERT [13] & 70.50 & 96.11 & 82.81 \\ PGAT-BERT [27] & 70.63 & 96.53 & 81.87 \\ ours & 71.81 & 96.40 & 82.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: F1 score statistics on the Weibo, Resume, and OntoNotes dataset. highest F1 score of 96.53% among all baseline models. Our model obtained the second-highest F1 score of 96.40%. Table 3 also displays the experimental results of various models on the OntoNotes dataset. BERT-LSTM-CRF and FLAT-BERT [9] both achieved an F1 score of 81.82%. The most recent polymorphic graph-based attention network, PGAT-BERT [27], achieves an F1 score of 81.87%. SoftLexicon-BERT [13] has the highest F1 score among the baseline models at 82.81%. We can see that our model obtained the highest F1 score of 82.99%. We also compared the E-commerce dataset with Multi-Graph [37], PGAT-BERT [27], and Bi-LSTM-CRF as the baseline. In addition, the Bi-LSTM-CRF methods were compared by adding three ground name table features, with the separate addition of N-gram features, position-independent entity type (PIET features), and position-dependent entity type (PDET features). The results are shown in Table 4.  
Table 4 shows the results on the E-commerce dataset. The classical BERT-LSTM-CRF obtains an F1 score of 73.6%. In addition, the F1 scores in Multi-Graph [37] and PGAT-BERT [27] are 75.2% and 80.7%, respectively. Consistent with the observations in the Weibo and OntoNotes datasets, our model obtained the highest F1 score of 81.8%. By observation, our model performs best on the Weibo dataset. This shows that our model can deal with data with a certain amount of noise. On the E-commerce dataset in the e-commerce domain, the model also achieves a significant improvement (1.1%). Since the E-commerce dataset only contains two types of entity labels, our model is more suitable for processing datasets with fewer types of entities.