A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Machine Learning
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Introduction to Stochastic Search and Optimization
Introduction to Stochastic Search and Optimization
Semi-supervised learning using randomized mincuts
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Evaluating machine learning for information extraction
ICML '05 Proceedings of the 22nd international conference on Machine learning
Large scale semi-supervised linear SVMs
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Relation extraction using label propagation based semi-supervised learning
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Solving multiclass support vector machines with LaRank
Proceedings of the 24th international conference on Machine learning
Pegasos: Primal Estimated sub-GrAdient SOlver for SVM
Proceedings of the 24th international conference on Machine learning
Label Propagation through Linear Neighborhoods
IEEE Transactions on Knowledge and Data Engineering
Adaptive information extraction from text by rule induction and generalisation
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
EKAW'10 Proceedings of the 17th international conference on Knowledge engineering and management by the masses
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We present an approach to automating knowledge extraction in the aerospace engineering domain which has had a fundamental impact on the way engineers manage their collective knowledge built with years of experience. Even though obtaining labelled data in this domain is hard due to the high cost of domain experts' time, the application of the machine learning-based technology was successful, yielding results comparable to the state-of-the-art. Moreover, we present a comparison between several machine learning approaches in extracting knowledge from reports about jet engines. We show that the application of a semi-supervised approach does not provide a significant increase in accuracy so as to justify its adoption due to its much higher computational cost, but that the application of a large-scale approach considerably reduces both training and testing time while keeping accuracy comparable to the standard supervised approach, making it a good choice for this class of application scenarios.