用深度学习分析飞行事故文本，自动判断飞机损伤等级。
Comparative Study of Deep Learning Architectures for Textual Damage Level Classification
- 对比LSTM、BLSTM、GRU和sRNN四种模型处理事故描述。
- 所有模型准确率超88%，sRNN表现最佳达89%。
- 适合航空安全分析、事故报告自动化处理场景。
鉴于航空业安全的极端重要性，即使微小的操作异常也可能带来严重后果。全面记录事件与事故有助于识别根本原因并提出安全措施。然而，事故叙述的非结构化特性给计算机系统解读带来挑战。本研究旨在利用自然语言处理（NLP）与深度学习模型分析此类叙述，对航空安全事件中的飞机损伤等级进行分类。通过实现LSTM、BLSTM、GRU和sRNN等深度学习模型，研究取得了良好成果：所有模型均表现优异，准确率超过88%，显著高于四分类问题的25%随机猜测基准。其中，sRNN在召回率和准确率上表现最佳，达到89%。这些发现凸显了NLP与深度学习模型从非结构化文本中提取可操作洞察的潜力，尤其适用于评估航空安全事件中飞机损伤程度。
原文摘要 · Abstract (English)
Given the paramount importance of safety in the aviation industry, even minor operational anomalies can have significant consequences. Comprehensive documentation of incidents and accidents serves to identify root causes and propose safety measures. However, the unstructured nature of incident event narratives poses a challenge for computer systems to interpret. Our study aimed to leverage Natural Language Processing (NLP) and deep learning models to analyze these narratives and classify the aircraft damage level incurred during safety occurrences. Through the implementation of LSTM, BLSTM, GRU, and sRNN deep learning models, our research yielded promising results, with all models showcasing competitive performance, achieving an accuracy of over 88% significantly surpassing the 25% random guess threshold for a four-class classification problem. Notably, the sRNN model emerged as the top performer in terms of recall and accuracy, boasting a remarkable 89%. These findings underscore the potential of NLP and deep learning models in extracting actionable insights from unstructured text narratives, particularly in evaluating the extent of aircraft damage within the realm of aviation safety occurrences.
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