# Extracting Domain-Specific Chinese Named Entities for Aviation Safety Reports: A Case Study
## 1 Introduction  
Data-driven frameworks for analyzing aviation safety events have recently received attention. The demand for safety information analysis by civil aviation safety personnel is constantly increasing. The Civil Aviation Administration of China (CAAC) attaches great importance to the management of civil aviation safety information and has established data systems such as Aviation Safety Information System, Service Difficulty Reporting System (SDR) and Bird Strike Database. However, the processing and mining of existing safety information has not seen sufficient traction. Efficiently extracting and processing safety information has emerged as a pressing imperative, necessitating a proactive response. Leveraging new-generation information technology empowers civil aviation safety personnel with an intuitive, real-time, and convenient means to process safety information, which leads to the enhanced management of civil aviation safety endeavors [1]. Named entityrecognition technology can effectively extract key entities from a text, thereby reducing interference from redundant information. So far, there are few studies that combine aviation safety with named entity recognition technology. Therefore, this paper attempts to conduct named entity recognition research on aviation safety to further promote academic progress in related fields. At the same time, by demonstrating the use of this technology to quickly mine important information in reports, it can help safety personnel improve efficiency. Safety reports are information collected about actual or potential safety defects. The purpose of safety reports is to improve the security of aircraft operations via the timely detection of operational hazards and system defects. Appropriate and remedial measures can be determined through the timely analysis of safety data, playing a significant role in the prevention of accidents. As shown in Figure 1, an aircraft may potentially encounter thunderstorms, icing, turbulence, and volcanic ash during its operation, which poses a substantial threat to the integrity and safety of the aircraft. Aviation safety reports should be used to record the date, aircraft type, operation, event, etc., of accidents. Utilizing specialized methodologies to analyze aviation safety reports can equip personnel across diverse departments with invaluable insights, thereby enhancing their expertise and bolstering the overall assurance of flight safety. The common bird strike issues are shown in Figure 2.  
Specific entity extraction refers to the process of identifying the boundaries of specific entities, extracting named entities with a specific meaning, and classifying them into pre-defined entity categories [2]. In general, pre-defined entity categories are usually the key information in a text. The task of specific entity extraction is to extract key information from a large amount of specific text information. Specific entity extraction plays a crucial role in  
Figure 1: Aviation flight hazards. Figure 2: Bird strike issues. domain fields such as medicine, aviation and agronomy. In addition, it can lay a valuable foundation for entity relationship extraction and knowledge graph construction. Specific entity extraction is usually considered a sequence labeling task and uses token-based classification techniques such as named entity recognition (NER). With the expansion of research fields, NER tasks are increasingly receiving attention, but there is still limited recognition of technology terms. Most NER studies only include general named entities such as date, location, and people, which are insufficient to reflect significant concepts in a specific research field. Terms are a type of more granular, meaningful and interpretable entity that better reflect the semantics of a text. In addition to general entities, aviation safety reports contain more terms with civil aviation significance.