A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Improved Boosting Algorithms Using Confidence-rated Predictions
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BoosTexter: A Boosting-based Systemfor Text Categorization
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Introduction to the CoNLL-2002 shared task: language-independent named entity recognition
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A joint model to identify and align bilingual named entities
Computational Linguistics
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This paper presents a system that applies boosting to the task of named-entity identification. The CoNLL-2002 shared task, for which the system is designed, is language-independent named-entity recognition. Using a set of features which are easily obtainable for almost any language, the presented system uses boosting to combine a set of weak classifiers into a final system that performs significantly better than that of an off-the-shelf maximum entropy classifier.