Named Entity Tagging a Very Large Unbalanced Corpus: Training and Evaluating NE Classifiers

We describe a systematic and application-oriented approach to training and evaluating named entity recognition and classification (NERC) systems, the purpose of which is to identify an optimal system and to train an optimal model for named entity tagging DeReKo, a very large general-purpose corpus of contemporary German (Kupietz et al., 2010). DeReKo {`}s strong dispersion wrt... (read more)

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