Article | Proceedings of the NoDaLiDa 2017 Workshop on Processing Historical Language | Normalizing Medieval German Texts: from rules to deep learning
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Title:
Normalizing Medieval German Texts: from rules to deep learning
Author:
Natalia Korchagina: Institute of Computational Linguistics, University of Zurich, Switzerland
Download:
Full text (pdf)
Year:
2017
Conference:
Proceedings of the NoDaLiDa 2017 Workshop on Processing Historical Language
Issue:
133
Article no.:
004
Pages:
12-17
No. of pages:
6
Publication type:
Abstract and Fulltext
Published:
2017-05-10
ISBN:
978-91-7685-503-4
Series:
Linköping Electronic Conference Proceedings
ISSN (print):
1650-3686
ISSN (online):
1650-3740
Series:
NEALT Proceedings Series
Publisher:
Linköping University Electronic Press, Linköpings universitet


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The application of NLP tools to historical texts is complicated by a high level of spelling variation. Different methods of historical text normalization have been proposed. In this comparative evaluation I test the following three approaches to text canonicalization on historical German texts from 15 th –16 th centuries: rule-based, statistical machine translation, and neural machine translation. Character based neural machine translation, not being previously tested for the task of normalization, showed the best results.

Proceedings of the NoDaLiDa 2017 Workshop on Processing Historical Language

Author:
Natalia Korchagina
Title:
Normalizing Medieval German Texts: from rules to deep learning
References:

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Proceedings of the NoDaLiDa 2017 Workshop on Processing Historical Language

Author:
Natalia Korchagina
Title:
Normalizing Medieval German Texts: from rules to deep learning
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Last updated: 2017-02-21