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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. genre, register and time forces us to base our decision for a specific NERC system on an evaluation performed on a representative sample of DeReKo instead of performance figures that have been reported for the individual NERC systems when evaluated on more uniform and less diverse data. We create and manually annotate such a representative sample as evaluation data for three different NERC systems, for each of which various models are learnt on multiple training data. The proposed sampling method can be viewed as a generally applicable method for sampling evaluation data from an unbalanced target corpus for any sort of natural language processing.

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Author:Joachim Bingel, Thomas Haider
Parent Title (English):Proceedings of the ninth conference on international language resources and evaluation (LREC’14)
Publisher:European Language Resources Association (ELRA)
Place of publication:Reykjavik
Document Type:Conference Proceeding
Year of first Publication:2014
Date of Publication (online):2014/10/13
Tag:Evaluation; Named entity recognition; Very large corpora
GND Keyword:Deutsches Referenzkorpus (DeReKo); Identitätsverwaltung; Korpus <Linguistik>; Textkorpus
Page Number:2578
First Page:2583
DDC classes:400 Sprache / 430 Deutsch
Open Access?:ja
Leibniz-Classification:Sprache, Linguistik
Licence (German):License LogoUrheberrechtlich geschützt