基于弱监督语义词典构建的航空安全事件报告原因识别
Cause Identification from Aviation Safety Incident Reports via Weakly Supervised Semantic Lexicon Construction
摘要：
航空安全报告系统收集自愿提交的航空安全事故报告，以促进旨在减少此类事故的研究工作。为了有效地减少这些事件，准确地确定这些事件发生的原因至关重要。更确切地说，在给定一系列可能的原因或形成因素的情况下，这一原因识别任务涉及识别所有且仅识别那些对报告中所述事件负责的形成因素。我们研究两种方法来识别原因。这两种方法都利用了语义词典提供的信息，语义词典是通过Thelen和Theloffs Basilisk框架自动构建的，并通过我们的语言和算法修改进行了增强。第一种方法使用简单的启发式标记报告，该方法在报告中查找语义词典学习过程中获得的单词和短语。第二种方法将原因识别重新定义为文本分类问题，采用监督和转换文本分类算法从标记有成形因素的事件报告中学习模型，并使用模型标记未见过的报告。我们的实验表明，无论是基于经验的方法和基于学习的方法（当有足够的训练数据）优于基线系统显着。
英文摘要：
The Aviation Safety Reporting System collects voluntarily submitted reports on aviation safety incidents to facilitate research work aiming to reduce such incidents. To effectively reduce these incidents, it is vital to accurately identify why these incidents occurred. More precisely, given a set of possible causes, or shaping factors, this task of cause identification involves identifying all and only those shaping factors that are responsible for the incidents described in a report. We investigate two approaches to cause identification. Both approaches exploit information provided by a semantic lexicon, which is automatically constructed via Thelen and Riloffs Basilisk framework augmented with our linguistic and algorithmic modifications. The first approach labels a report using a simple heuristic, which looks for the words and phrases acquired during the semantic lexicon learning process in the report. The second approach recasts cause identification as a text classification problem, employing supervised and transductive text classification algorithms to learn models from incident reports labeled with shaping factors and using the models to label unseen reports. Our experiments show that both the heuristic-based approach and the learning-based approach (when given sufficient training data) outperform the baseline system significantly.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
