Boosting for named entity recognition

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.