# Research on Chinese Named Entity Recognition Based on Lexical Information and Spatial Features
## 6 Conclusions  
In this study, this paper introduces a study of Chinese NER based on lexical information and spatial features. The method cleverly combines Bi-LSTM and the local attention mechanism to effectively mitigate lexical matching conflicts and extract spatial relationships between characters, thus enhancing the ability of named entity recognition. The method of this paper mainly includes two aspects: firstly, through the Bi-LSTM gating mechanism, we obtain the global bidirectional semantic information and sequential temporal information of the conflict matching vocabulary, reduce the weight of the conflict matching vocabulary, and mitigate the effects of entity disambiguation after fusion with characters. Secondly, the local attention mechanism is used to make full use of the local spatial relationship between the unfused vocabulary information characters and the fused vocabulary information characters, and the local spatial relationship of the unfused vocabulary information characters is further extracted in order to solve the problem of ineffective vocabulary enhancement methods for some characters. The experiments provide evidence that the SISF model outperforms the established benchmark model by a substantial margin in the context of Chinese named entity recognition. Additionally, ablation experiments were conducted to comprehensively investigate the influence of various modules on the model's performance. In the future, this paper will explore the following directions: (1) introducing structural features of Chinese characters, exploring the fusion methods between features, and highlighting the advantages of different features; (2) character and non-entity matching vocabulary fusion reduces the model's performance, and knowing how to reduce the introduction of the non-entity matching vocabulary is also the direction of this paper's subsequent research.  
Conceptualization, Z.Z.; Methodology, Z.Z.; Software, Z.Z. and S.L.; Validation, Z.Z.; Formal analysis, Z.Z. and Z.J.; Investigation, Z.Z. and H.Y.; Resources, Z.Z., S.L. and H.Y.; Data curation, Z.Z. and Z.J.; Writing--original draft, Z.Z. and S.L.; Writing--review & editing, Z.Z.; Visualization, Z.Z.; Supervision, Z.Z. and S.L.; Project administration, Z.Z. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the National Key R&D Program of China (2022 ZD0115801), Major Science and Technology Projects in Xinjiang Uygur Autonomous Region (2022A02012-1), and the National Natural Science Foundation of China (61966034).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Model** & **Ontonotes4.0** & **Weibo** \\ \hline BERT+FLAT+Bi-LSTM & **82.45** & **70.89** \\ BERT+FLAT+CNN & 82.21 & 69.35 \\ BERT+FLAT & 81.82 & 68.55 \\ LR-CNN & 74.45 & 59.92 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Experimental results of Bi-LSTM vs. CNN comparison.  
**Acknowledgments:** The authors would like to thank the anonymous reviewers for their contribution to this article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.