# Research on Chinese Named Entity Recognition Based on Lexical Information and Spatial Features
## 5 Results
### Comparison Experiment  
The specific experimental results of this paper are shown in Tables 3 and 4, where \(BERT+FLAT_{this}\) is the experimental result of Flat-Lattice on the NVIDIA GeForce RTX 3090 card. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**MSRA**} & \multicolumn{4}{c}{**Ontonotes4.0**} \\ \cline{2-7}  & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline FGN & 95.45 & 95.81 & 95.64 & **82.61** & 81.48 & 82.04 \\ Token-Relation & 96.08 & **96.18** & 96.13 & 82.57 & 83.99 & **83.28** \\ LR-CNN & - & - & 93.71 & - & - & 74.45 \\ PLTE+BERT & 94.91 & 94.15 & 94.53 & 79.62 & 81.82 & 80.60 \\ SLK-NER & - & - & - & 77.90 & 82.20 & 80.20 \\ BERT+FLAT & - & - & 96.09 & - & - & 81.82 \\ \(BERT+FLAT_{this}\) & 95.94 & 96.00 & 95.97 & 80.66 & 82.76 & 81.70 \\ SISF & **96.69** & 95.99 & **96.34** & 80.21 & **85.51** & 82.77 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: MSRA and Ontonotes4.0. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Weibo**} & \multicolumn{4}{c}{**Resume**} \\ \cline{2-7}  & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline FGN & 69.02 & **73.65** & 71.25 & 96.49 & 97.08 & 96.79 \\ Token-Relation & **72.82** & 66.02 & 69.62 & 96.01 & 96.50 & 96.36 \\ LR-CNN & - & - & 59.92 & - & - & 95.11 \\ PLTE+BERT & 72.00 & 66.67 & 69.23 & 96.16 & 96.75 & 96.45 \\ SLK-NER & 61.80 & 66.30 & 64.00 & 95.20 & 96.40 & 95.80 \\ BERT+FLAT & - & - & 68.55 & - & - & 95.86 \\ \(BERT+FLAT_{this}\) & 68.60 & 69.66 & 69.13 & 95.68 & 96.56 & 96.12 \\ SISF & 71.32 & 73.20 & **72.25** & **96.75** & **96.93** & **96.84** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Weibo and Resume. An analysis of the experimental results reveals that the SISF model demonstrates improvement in Chinese named entity recognition (NER) across all four datasets relative to the baseline model, Flat-Lattice. Weibo: As shown in Table 3, the F1 score for NER in the Weibo dataset exhibited growth of 3.12%. The results indicate that compared to the other three datasets, the SISF model exhibits a significant performance enhancement in NER on the relatively minor Weibo dataset. This phenomenon arises due to the pronounced decline in named entity recognition performance in the Weibo dataset compared to the other three datasets when transitioning from the Transformer model to the Bi-LSTM model [26]. This suggests that the SISF model contributes to NER by mitigating conflicts between matched vocabularies and enhancing the local spatial relationships between characters with unfused and fusion vocabulary information. Moreover, compared to the Flat-Lattice method, the SISF model also compensates for the underfitting issue of the Transformer model in small datasets, thereby further improving Chinese NER performance. Resume: As indicated in Table 3, the F1 score for NER in the Resume dataset demonstrated an enhancement of 0.72%. In contrast, the F1 score for the Flat-Lattice model on the same dataset is 95.86%. This dataset uses vocabulary information to enhance named entity recognition through lexical enhancement methods, but the enhancement is more limited than the other three datasets [12]. The experiments conducted in this article have substantiated that extracting local spatial relationships between characters from the non-integrated vocabulary and characters from the integrated vocabulary yields superior results in named entity recognition, in comparison to mitigating vocabulary matching conflicts. This further confirms that in vocabulary enhancement methods, when vocabulary information is insufficient, the accuracy of named entity recognition can be effectively improved by considering the local spatial relationship between characters of unfused and fused vocabulary. Ontonotes 4.0: As shown in Table 4, the F1 score for NER in the Ontonotes 4.0 dataset increased by 1.07%.