Published: 2017-05-10
ISBN: 978-91-7685-503-4
ISSN: 1650-3686 (print), 1650-3740 (online)
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.
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