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

Joachim Bingel, Thomas Haider

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Abstract
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.
Anthology ID:
L14-1728
Volume:
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)
Month:
May
Year:
2014
Address:
Reykjavik, Iceland
Editors:
Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Hrafn Loftsson, Bente Maegaard, Joseph Mariani, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
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Pages:
Language:
URL:
http://www.lrec-conf.org/proceedings/lrec2014/pdf/967_Paper.pdf
DOI:
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Cite (ACL):
Joachim Bingel and Thomas Haider. 2014. Named Entity Tagging a Very Large Unbalanced Corpus: Training and Evaluating NE Classifiers. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), Reykjavik, Iceland. European Language Resources Association (ELRA).
Cite (Informal):
Named Entity Tagging a Very Large Unbalanced Corpus: Training and Evaluating NE Classifiers (Bingel & Haider, LREC 2014)
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PDF:
http://www.lrec-conf.org/proceedings/lrec2014/pdf/967_Paper.pdf
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