Abstract
Automatically authoring or acquiring cases in the case-based reasoning (CBR) systems is recognized as a bottleneck issue that can determine whether a CBR system will be successful or not. In order to reduce human effort required for authoring the cases, we propose a framework for authoring the case from the unstructured, free-text, historic maintenance data by applying natural language processing technology. This paper provides an overview of the proposed framework, and outlines its implementation, an automated case creation system for the Integrated Diagnostic System. Some experimental results for testing the framework are also presented.
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Yang, C., Orchard, R., Farley, B., Zaluski, M. (2003). Authoring Cases from Free-Text Maintenance Data. In: Perner, P., Rosenfeld, A. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2003. Lecture Notes in Computer Science, vol 2734. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45065-3_12
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DOI: https://doi.org/10.1007/3-540-45065-3_12
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