# Extracting Domain-Specific Chinese Named Entities for Aviation Safety Reports: A Case Study
## 1 Introduction
The automatic extraction of civil aviation safety report terms refers to the named entity recognition of terms with specific civil aviation meanings such as aircraft type, operation, and aircraft part. The research object of this paper is the case study of aviation safety reports obtained from an aviation company, which are text files recorded for aviation on a weekly basis. An aviation safety report corresponding to a flight includes a textual description of the event. Each file records safety events that occurred within a week. The text length of each event varies considerably. The analysis of the events within the aviation safety reports plays a crucial role in furthering aviation safety, as it can be used to discover defects and research human performance among other things. By extracting meaningful and interpretable entities from safety events, it helps relevant personnel to quickly understand the process of safety events and to summarize experiences. The dataset of aviation safety reports used in the paper is from 2014 to 2016 and comes from an airline. The shortest sentence length in the dataset is 73, the longest sentence length is 1860, and the average sentence length is 166. Ten kinds of entities and sequences, such as event, company, city, operation, date, aircraft type, personnel, flight number, aircraft registration and aircraft part, were annotated using the BIO format. By analyzing the important entities in the reports, detailed background information is provided for the event investigation. This helps investigators determine the cause of the event and provides a basis for developing corresponding preventive measures. At present, although NER has been well applied in many domains, such as medicine and agriculture, its deployment in digital aviation is very limited. [PERSON] et al. [3] pointed out that airport abnormal event reports are usually stored in an unstructured manner, which is inconvenient for relevant personnel to conduct further analysis. They proposed the use of the Chinese named entity recognition model to structurally process texts and extract key information. [PERSON] et al. [4] effectively and quickly obtained key information, such as the uncivilized penalties, deadlines and behaviors of on-board civil aviation passengers for various uncivilized behaviors information using a model. [PERSON] et al. [5] aimed to obtain the large number of nested and compound named entities in civil aviation enterprise entities, and spliced overlapping dilated convolutional neural network (ODCNN) and bi-directional long short-term memory (BiLSTM) models in different ways to identify different categories of operations, commercial services and general services. However, their results are still limited in the number of aviation entity categories and their efficiency. Meaningful entities are not proper-name entities, which leads to the difficulty of identifying them from unstructured text. Therefore, an efficient approach to extracting entities in a specific domain is required. This paper conducts research on Chinese specific entity extraction methods for aviation safety reports. Based on existing aviation safety reports, the dataset-aviation safety event is constructed. In view of the insufficient capture of entity boundary information via the general named entity recognition model and the strong professionalism of the research object in the paper, a specific named entity recognition method for aviation safety events that integrates enhanced representation through knowledge integration embedding (ERNIE), domain dictionary, general dictionary, glyph information, and pinyin information is used. We use the ERNIE model for character embedding to obtain character vectors. By training pinyin vectors and glyph vectors separately, we enhance the performance of character embedding vector representation via the integration of these three vectors. In order to incorporate domain word information, we introduce a domain dictionary and trained it to obtain word vectors.