Authors: Anil Yelundur; Chris Giannella; Karine Megerdoomian; Craig Pfeifer
Addresses: Amazon India, Brigade Gateway, 8th floor, 26/1, Dr. Rajkumar Road, Malleshwaram (W), Bangalore – 560055, India ' The MITRE Corporation, 7515 Colshire Dr., McLean, VA, 22102, USA ' The MITRE Corporation, 7515 Colshire Dr., McLean, VA, 22102, USA ' The MITRE Corporation, 7515 Colshire Dr., McLean, VA, 22102, USA
Abstract: When adverse aviation events occur, narrative reports describing the events and their associated flights provide a valuable record for improving safety. Manual examination of large collections of such reports is challenging. Tools for automated event classification (assignment of type labels to individual reports) can help to mitigate this challenge. While several studies have developed and systematically empirically evaluated event classification tools on English aviation narratives, we are not aware of any that have done the same on foreign language narratives. We developed and implemented an approach for event classification based on Bayesian logistic regression and a novel feature selection technique. For comparison purposes, we also implemented an approach described in the literature. We collected and annotated a corpus of Japanese aviation incident reports, as well as, a corpus of French incident reports. We carried out a series of experiments comparing the accuracy of our approach and the other approach.
Keywords: aviation safety; text document classification; Bayesian logistic regression; BLR; automated event classification; foreign language reports; aviation reports; feature selection; adverse aviation events; aviation incidents; Japan; France.
International Journal of Knowledge Engineering and Data Mining, 2016 Vol.4 No.1, pp.54 - 73
Available online: 06 Feb 2017 *Full-text access for editors Access for subscribers Purchase this article Comment on this article