Extracting Domain-Specific Chinese Named Entities for Aviation Safety Reports: A Case Study
Source abstract
Abstract
Aviation safety reports can provide detailed records of past aviation safety accidents, analyze their problems and hidden dangers, and help airlines and other aviation enterprises avoid similar accidents from happening again. In a novel way, we plan to use named entity recognition technology to quickly mine important information in reports, helping safety personnel improve efficiency. The development of intelligent civil aviation creates demands for the incorporation of big data and artificial intelligence. Because of the aviation-specific terms and the complexity of identifying named entity boundaries, the mining of aviation safety report texts is a challenging domain. This paper proposes a novel method for aviation safety report entity extraction. First, 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. Second, we present a semantic representation enhancement approach through the fusion of enhanced representation through knowledge integration embedding (ERNIE), pinyin embedding and glyph embedding. Then, in order to improve the accuracy of specific entity extraction, we constructed and utilized the aviation domain dictionary which includes high-frequency technical aviation terms. After that, we adopted bilinear attention networks (BANs), the feature fusion approach originally used in multi-modal analysis, in our study to incorporate features extracted from both iterated dilated convolutional neural network (IDCNN) and bi-directional long short-term memory (BiLSTM) architectures. A case study of specific entity extraction for an aviation safety events dataset was conducted. The experimental results demonstrate that our proposed algorithm, with an F1 score reaching 97.93%, is superior to several baseline and advanced algorithms. Therefore, the proposed approach offers a robust methodological foundation for the relationship extraction and knowledge graph construction of aviation safety reports.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Topic Modeling · Risk and Safety Analysis · Sentiment Analysis and Opinion Mining
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Computer Science, Civil Aviation Flight University of China, Guanghan 618307, China
School of Economics and Management, Civil Aviation Flight University of China, Guanghan 618307, China
Department of Information Management, Air China, Chongqing 401120, China
Funding
the R&D Program of Key Laboratory of Flight Techniques and Flight Safety, CAAC · the Fundamental Research Funds for the Central Universities
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