# Extracting Domain-Specific Chinese Named Entities for Aviation Safety Reports: A Case Study
## 5 Conclusions  
In this paper, we proposed a novel model based on fusion with a double dictionary, fusion character features and bilinear attention networks. Outstanding performance and results were obtained from the self-build aviation safety events dataset. By fusing pinyin embedding vectors and glyph embedding vectors into character vectors, character vectors can learn richer character semantic information. By incorporating the aviation domain dictionary and the general dictionary at the embedding level, the model can better extract entity boundaries and identify correct classification results. Compared with the traditional fusion method, the BAN dynamic feature fusion method used in this paper can achieve better results without increasing the training time, which has certain research significance. We compare our approach to some baseline and advanced methods. Our experimental results indicate superior performance across all used evaluation measures. However, there are also some shortcomings that need to be addressed in future research. The F1 score of the aircraft part entity is relatively low due to the large number of aircraft part entities and the many nested entities. In the future, it is necessary to increase the relevant data to build a larger aviation safety events dataset, and to build a larger domain dictionary to further improve the performance of the model. Empirical results indicate that our approach is superior to several baseline and advanced approaches, laying a meaningful foundation for the subsequent relationship extraction and knowledge graph construction of aviation safety. At present, this study is still in the research stage. In fact, we will consider integrating it into safety analysis software for use by safety personnel in future research. In the future, we may continue to study the relationships between entities and further establish a knowledge graph.  
[PERSON] and Z.G.: conceptualization, methodology, validation, and writing--review and editing; Y.X.: conceptualization, methodology, and writing--review and editing; B.L. and T.Z.: methodology and writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research was partially supported by the R&D Program of Key Laboratory of Flight Techniques and Flight Safety, CAAC (No. FZ2022 ZZ01), the Fundamental Research Funds for the Central Universities (No. J2022-048).  
Not applicable.  
The dataset used in this paper is provided by an airline and it is confidential.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Entity** & **Precision** & **Recall** & **F1 Score** \\ \hline Event & 0.9481 & 0.9639 & 0.9560 \\ Company & 1.0000 & 1.0000 & 1.0000 \\ City & 1.0000 & 1.0000 & 1.0000 \\ Operation & 0.9748 & 0.9845 & 0.9796 \\ Date & 1.0000 & 1.0000 & 1.0000 \\ Aircraft Type & 0.9969 & 1.0000 & 0.9984 \\ Personnel & 0.9940 & 0.9970 & 0.9955 \\ Flight Number & 1.0000 & 1.0000 & 1.0000 \\ Aircraft Registration & 0.9969 & 0.9969 & 0.9969 \\ Aircraft Part & 0.8736 & 0.8736 & 0.8736 \\ \hline \hline \end{tabular}
\end{table}
Table 9: The results of each entity in the model.