Text-mining on incident reports to find knowledge on industrial safety

T. Nakata
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引用次数: 16

Abstract

To prevent accidents, it is very important to learn why and how past accidents occurred and escalated. The information of accidents is mostly recorded in natural language texts, which is not convenient to analyze the flow of events in the accidents. This paper proposes a method to recognize typical flow of events in a large set of text reports. By focusing two adjacent sentences, our system succeeded to detect typical pairs of predecessor word and successor word. Then we can recognize the typical flows of accidents.
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对事故报告进行文本挖掘,以找到有关工业安全的知识
为了防止事故的发生,了解过去事故发生和升级的原因和方式是非常重要的。事故信息大多以自然语言文本形式记录,不便于分析事故中的事件流程。本文提出了一种识别大量文本报告中典型事件流的方法。通过对两个相邻的句子进行聚焦,我们的系统成功地检测出了典型的前导词和后继词对。然后我们就能识别出典型的事故流程。
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