Trading MIPS and memory for knowledge engineering
Communications of the ACM
Finding factors: learning to classify case opinions under abstract fact categories
Proceedings of the 6th international conference on Artificial intelligence and law
Learning to classify text from labeled and unlabeled documents
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Extracting Knowledge from Diagnostic Databases
IEEE Expert: Intelligent Systems and Their Applications
Knowledge Extraction and Summarization for an Application of Textual Case-Based Interpretation
ICCBR '07 Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
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A valuable source of field diagnostic information for equipment service resides in the text notes generated during service calls. Intelligent knowledge extraction from such textual information is a challenging task. The notes are characterized by misspelled words, incomplete information, cryptic technical terms, and non-standard abbreviations. In addition, very few of the total number of notes generated may be diagnostically useful. We present an approach for identifying diagnostically relevant notes from the many raw field service notes and information is presented in this paper. N-gram matching and supervised learning techniques are used to generate recommendations for the diagnostic significance of incoming service notes. These techniques have potential applications in generating relevant indices for textual CBR.