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Proceedings of the Ninth Conference on Computational Natural Language Learning - CONLL '05
We report on an active learning experiment for named entity recognition in the astronomy domain. Active learning has been shown to reduce the amount of labelled data required to train a supervised learner by selectively sampling more informative data points for human annotation. We inspect double annotation data from the same domain and quantify potential problems concerning annotators' performance. For data selectively sampled according to different selection metrics, we find lowerdoi:10.3115/1706543.1706569 fatcat:3veiujcdzzc57l3vh4vrr5egqa