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Information Extraction from Helicopter Maintenance Records as a Springboard for the Future of Maintenance Text Analysis

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Trends in Applied Intelligent Systems (IEA/AIE 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6096))

Abstract

This paper introduces a novel application of information extraction techniques to extract data from helicopter maintenance records to populate a database. The goals of the research are to preprocess the text-based data for further use in data mining efforts and to develop a system to provide a rough analysis of generic maintenance records to facilitate in the development of training corpora for use in machine-learning for more refined information extraction system design. The Natural Language Toolkit was used to implement partial parsing of text by way of hierarchical chunking of the text. The system was targeted towards inspection descriptions and succeeded in extracting the inspection code, description of the part/action, and date/time information with 80.7% recall and 89.9% precision.

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References

  1. Kraus, S., Blake, C., West, S.L.: Information extraction from medical notes. In: Proceedings of the 12th World Congress on Health (Medical) Informatics – Building Sustainable Health Systems (MedInfo), Brisbane, Australia, pp. 1662–1664 (in press)

    Google Scholar 

  2. Hripcsak, G., Griedman, C., Alderson, P.O., DuMouchel, W., Johnson, S.B., Clayton, P.D.: Unlocking clinical data from narrative reports: A study of natural language processing. Annals of Internal Medicine 122, 681–688 (1995)

    Google Scholar 

  3. Gaizauskas, R., Harkema, H., Hepple, M., Setzer, A.: Task-oriented extraction of temporal information: The case of clinical narratives. In: Thirteenth International Symposium on Termporal Representation and Reasoning (TIME’06), pp. 188–195 (2006)

    Google Scholar 

  4. Irvine, A.K.: Natural language processing and temporal information extraction in emergency department triage notes. A Master’s Paper (2008)

    Google Scholar 

  5. Han, B., Gates, D., Levin, L.: Understanding temporal expressions in emails. In: Proceedings of the Human Language Technology Conference of the North American Chapter of the ACL, pp. 136–143 (2006)

    Google Scholar 

  6. Cardie, C.: Empirical methods in information extraction. AI Magazine 18(4), 65–79 (1997)

    Google Scholar 

  7. Bayoumi, A., Goodman, N., Shah, R., Eisner, L., Grant, L., Keller, J.: Conditioned-based maintenance at USC – part II: Implementation of CBM through the application of data source integration. Presented at the American Helicopter Society Specialists’ Meeting on Condition Based Maintenance. Huntsville, AL (2008)

    Google Scholar 

  8. Bird, S.G., Loper, E.: NLTK: The Natural Language Toolkit. In: Proceedings, 42nd Meeting of the Association for Computational Linguistics (Demonstration Track), Barcelona, Spain (2004)

    Google Scholar 

  9. Madnani, N.: Getting started on natural language processing with Python. ACM Crossroads 13(4) (2007)

    Google Scholar 

  10. Abney, S.: Partial parsing via finite-state cascades. Journal of Natural Language Engineering 2(4), 337–344 (1996)

    Article  Google Scholar 

  11. Cowie, J., Lehnert, W.: Information extraction. Communications of the ACM 39(1), 80–91 (1996)

    Article  Google Scholar 

  12. Marcus, M.P., Santorini, B., Marcinkiewicz, M.A.: Building a large annotated corpus of English: the Penn Treebank. Computational Linguistics 19(2), 313–330 (1993)

    Google Scholar 

  13. Bird, S., Klein, E., Loper, E.: Natural language processing with python: analyzing text with the Natural Language Toolkit. O’Reilly Media, Sebastopol (2009)

    MATH  Google Scholar 

  14. Jurafsky, D., Martin, J.H.: Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition. Pearson Education, Inc., Upper Saddle River (2009)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of South Carolina, Columbia, South Carolina, U.S.A.

    Amber McKenzie & Manton Matthews

  2. Department of Mechanical Engineering, University of South Carolina, Columbia, South Carolina, U.S.A.

    Nicholas Goodman & Abdel Bayoumi

  3. Condition-Based Maintenance Center, University of South Carolina, Columbia, South Carolina, U.S.A.

    Amber McKenzie, Nicholas Goodman & Abdel Bayoumi

Authors
  1. Amber McKenzie
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  2. Manton Matthews
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  3. Nicholas Goodman
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  4. Abdel Bayoumi
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Editor information

Editors and Affiliations

  1. Dept. of Computing and Numerical Analysis, University of Cordoba, Campus Universitario de Rabanales, Einstein Building, 3rd floor, 14071, Cordoba, Spain

    Nicolás García-Pedrajas

  2. Dept. of Computer Science and Artificial Intelligence, ETS de Ingenierias Informática y de Telecomunicación, University of Granada, 18071, Granada, Spain

    Francisco Herrera

  3. School of Computing, University of the West of Scotland, PA1 2BE, Paisley, UK

    Colin Fyfe

  4. Dept. Computer Science and Artificial Intelligence, ETS de Ingenierias Informática y de Telecomunicación, University of Granada, 18071, Granada, Spain

    José Manuel Benítez

  5. Department of Computer Science, Texas State University-San Marcos, 601 University Drive, TX 78666-4616, San Marcos, USA

    Moonis Ali

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© 2010 Springer-Verlag Berlin Heidelberg

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McKenzie, A., Matthews, M., Goodman, N., Bayoumi, A. (2010). Information Extraction from Helicopter Maintenance Records as a Springboard for the Future of Maintenance Text Analysis. In: García-Pedrajas, N., Herrera, F., Fyfe, C., Benítez, J.M., Ali, M. (eds) Trends in Applied Intelligent Systems. IEA/AIE 2010. Lecture Notes in Computer Science(), vol 6096. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13022-9_59

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  • DOI: https://doi.org/10.1007/978-3-642-13022-9_59

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13021-2

  • Online ISBN: 978-3-642-13022-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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