Linguistic text mining for problem reports

  • Authors:

  • Jane T. Malin;David R. Throop;Christopher Millward;Hansen A. Schwarz;Fernando Gomez;Carroll Thronesbery

  • Affiliations:

  • NASA Johnson Space Center, Houston;The Boeing Company, Houston;School of Electrical Engineering and Computer Science, University of Central Florida, Orlando;School of Electrical Engineering and Computer Science, University of Central Florida, Orlando;School of Electrical Engineering and Computer Science, University of Central Florida, Orlando;S&K Aerospace, Incorporated, Houston

  • Venue:
  • SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
  • Year:
  • 2009

Quantified Score

Hi-index 0.00

Visualization

Abstract

This paper describes a linguistic text mining tool for analyzing problem reports in aerospace engineering and safety organizations. The Semantic Trend Analysis Tool (STAT) helps analysts find and review recurrences, similarities and trends in problem reports. The tool is being used to analyze engineering discrepancy reports at NASA Johnson Space Center. The tool has been augmented with a statistical natural language parser that also resolves parsing gaps and identifies verb arguments and adjuncts. The tool uses an aerospace ontology augmented with features of taxonomies and thesauruses. The ontology defines hierarchies of problem types, equipment types and function types. STAT uses the output of the parser and the aerospace ontology to identify words and phrases in problem report descriptions that refer to types of hazards, equipment damage, performance deviations or functional impairments. Tool performance has been evaluated on 120 problem descriptions from problem reports, with encouraging results.