Boosting a weak learning algorithm by majority
COLT '90 Proceedings of the third annual workshop on Computational learning theory
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Exploring semantic groups through visual approaches
Journal of Biomedical Informatics - Special issue: Unified medical language system
Biomedical named entity recognition using conditional random fields and rich feature sets
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Assessing the practical usability of an automatically annotated corpus
LAW V '11 Proceedings of the 5th Linguistic Annotation Workshop
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Among the many proposals to promote alternatives to costly to create gold standards, just recently the idea of a fully automatically, and thus cheaply, to set up silver standard has been launched. However, the current construction policy for such a silver standard requires crucial parameters (such as similarity thresholds and agreement cut-offs) to be set a priori, based on extensive testing though, at corpus compile time. Accordingly, such a corpus is static, once it is released. We here propose an alternative policy where silver standards can be dynamically optimized and customized on demand (given a specific goal function) using a gold standard as an oracle.