Natural language processing for aviation safety reports: From classification to interactive analysis

Abstract

In this paper we describe the different NLP techniques designed and used in collaboration between the CLLE-ERSS research laboratory and the CFH / Safety Data company to manage and analyse aviation incident reports. These reports are written every time anything abnormal occurs during a civil air flight. Although most of them relate routine problems, they are a valuable source of information about possible sources of greater danger. These texts are written in plain language, show a wide range of linguistic variation (telegraphic style overcrowded by acronyms or standard prose) and exist in different languages, even for a single company/country (although our main focus is on English and French). In addition to their variety, their sheer quantity (e.g. 600/month for a large airline company) clearly requires the use of advanced NLP and text mining techniques in order to extract useful information from them. Although this context and objectives seem to indicate that standard NLP techniques can be applied in a straightforward manner, innovative techniques are required to handle the specifics of aviation report text and the complex classification systems. We present several tools that aim at a better access to this data (classification and information retrieval), and help aviation safety experts in their analyses (data/text mining and interactive analysis).Some of these tools are currently in test or in use both at the national and international levels, by airline companies as well as by regulation authorities (DGAC 1 , EASA 2 , ICAO 3 ).

Text analytics in industry: Challenges, desiderata and trends

“…First are those aimed at alleviating human effort in tedious and time-consuming tasks, thereby enabling human experts to focus on more critical or value-added activities. A typical example of such applications is that of text classification, as presented in [15,16]. Second are those applications that aim at facilitating and enhancing the quality of the information-seeking process of end users and the results, such as the CBR application that accepts natural language question from users [17] and the information retrieval component of the application in [16].…”

“…Our review of the current state-of-the-art will be based on the scientific articles published in this special issue of the Computers in Industry journal 2 on text analytics in industry, namely [12][13][14][15][16][17][18].…”

NLP Tools Used in Civil Aviation: A Survey

“…air travel safety is continuously improved. In 2012 ICAO (2013) reported 2012 as have had the least rate of incidents and accidents (3.2 accidents for one million arrivals and departures) [11] In most of the cases, even when something serious occurs, example engine problems or Gear and hydraulics(front and rear) problems, the accident is avoided and the aircraft is able to land safely. Equipments such as ILS, ADS-B, Primary and secondary radar, training in Simulators and safety procedures imposed all throughout has contributed to safety.…”

“…[11] 2.6.1 ASRS ASRS products and services are as follows: Table 6. we see that there is a coverage gap for six IACO languages (English, French, Chinese, Arabic, Spanish, Russian) In this paper we have come across some NLP tools that is helping various Aircraft manufacturers produce crafts faster and better and also some NLP tools that help us in storingincident reports and help us to have a safer future for aircrafts by helping to identify the problems and how to avoid the incidents and in turn accidents.…”

Relevancer: Finding and Labeling Relevant Information in Tweet Collections

“…Enabling human intervention is crucial to ensuring high level performance in text analytics approaches [9]. Therefore we need to build a flexible pipeline that can facilitate human input in order to yield customized results [2].…”