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Deep learning for extracting word-level meaning from safety report narratives

机译:深度学习从安全报告叙述中提取词级含义

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摘要

Much of aviation safety reporting data consists of structured data e.g., digital flight data or radar data. However, safety report narratives, which come in the form of unstructured text data, are indispensable for safety reporting. Structured data alone is inadequate to capture all of the details of an incident while narratives can and do represent a myriad of details in a form that is natural for analysts to work with. However, large-scale analysis of narratives comes with many challenges: 1) it is difficult to employ enough human experts to digest the continuous flow of new incident reports 2) authors of incident reports use many different terms to refer to the same semantic concept, which makes it more difficult to determine if a specific concept occurs in texts 3) authors often make spelling mistakes and 4) authors use a wide variety of abbreviations for terms, some of which are nonstandard.
机译:许多航空安全报告数据由结构化数据组成,例如数字飞行数据或雷达数据。但是,以非结构化文本数据形式出现的安全报告叙述对于安全报告来说是必不可少的。仅结构化数据不足以捕获事件的所有细节,而叙述可以并且确实以分析人员可以自然地使用的形式表示大量细节。但是,对叙事进行大规模分析面临许多挑战:1)很难聘请足够的人类专家来消化新事件报告的持续流向2)事件报告的作者使用许多不同的术语来指代相同的语义概念,这使得确定文本中是否存在特定概念变得更加困难3)作者经常犯拼写错误,并且4)作者使用了各种各样的缩写词,其中有些是非标准的。

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