# Research on Chinese Named Entity Recognition Based on Lexical Information and Spatial Features
## 5 Results
### Ablation Experiment  
In this research paper, ablation experiments were conducted to confirm the effectiveness of each component of the proposed methodology. The outcomes of these experiments are depicted in Table 5:  
Through the ablation experiments in this paper, the model's ability for named entity recognition generally decreases when the local attention module is removed, thus validating the importance of further extracting local spatial features between unfused lexical information and fused lexical information at the character level. In particular, for a small dataset like Weibo, where the model's training capability is underfitted, the model performs best in named entity recognition by extracting spatial relationships between characters compared to the other three datasets.  
In addition, to illustrate the superior capability of the SISF model in mitigating conflicts between matching words and enhancing named entity recognition, this paper performs experimental comparisons with [PERSON] et al. [9], who proposed to use CNNs to feedback high-level features to resolve the conflicts of matching words by refining the network model, as shown in Table 6. The experimental findings indicate that, in contrast to the Bi-LSTM model introduced in this study, the utilization of CNN to extract enhanced features from matching vocabulary is less efficacious at mitigating conflicts among matching words.  
The advantage of the Bi-LSTM model is its ability to capture the global bidirectional semantic information and sequential temporal information of lexical matching. When dealing with conflicting matching words, Bi-LSTM models the sequence of matching words in the whole sentence. The incorporation of the gating mechanism enables the effective capture of global bidirectional semantic information and sequential temporal information  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Model** & **Ontonotes4.0** & **Resume** & **MSRA** & **Weibo** \\ \hline SISF & **82.77** & **96.84** & **96.34** & **72.25** \\ BERT+FLAT+Bi-LSTM & 82.45 & 96.43 & 96.14 & 70.89 \\ \(BERT+FLAT\)_tilis_ & 81.82 & 96.12 & 95.97 & 69.13 \\ BERT+FLAT & 81.82 & 95.86 & 96.09 & 68.55 \\ \hline \hline \end{tabular}
\end{table}
Table 5: F1 values for NER for each dataset after reducing the weight of conflicting terms.  
Figure 6: Attentional visualization: (**a**) represents the visualization of the score matrix of characters with fused vocabulary information versus those with unfused lexical information after local attention, and (**b**) represents the visualization of the score matrix of characters with fused lexical information versus those with unfused lexical information without local attention. (**a**) Through local attention. (**b**) Without local attention.  
within the sequence of matching words. In contrast, a convolutional neural network (CNN) mainly focuses on local features within the coverage of the convolutional kernel. It performs convolutional operations by sliding the convolutional kernel over the matching vocabulary, thus capturing the local features of the matching vocabulary. However, in terms of capturing the semantic and sequential temporal information of the matching vocabulary in the whole sentence, the performance of CNN is weaker compared to Bi-LSTM. Therefore, Bi LSTM is superior to CNN in solving vocabulary-matching conflicts and improving named entity recognition capabilities.