Text preprocessing is not only an essential step to prepare the corpus for modeling but also
a key area that directly affects the natural language processing (NLP) application results. For …

Part-of-speech tagging, morphological tagging, and lemmatization of historical texts pose
special challenges due to the high spelling variability and the lack of large, high-quality …

Historical text archives constitute a rich and diverse source of information, which is
becoming increasingly readily accessible, due to large-scale digitisation efforts. However, it …

In this paper, we apply different NMT models to the problem of historical spelling
normalization for five languages: English, German, Hungarian, Icelandic, and Swedish. The …

We present a multilingual evaluation of approaches for spelling normalisation of historical
text based on data from five languages: English, German, Hungarian, Icelandic, and …

Abstract Pettersson, E. 2016. Spelling Normalisation and Linguistic Analysis of Historical
Text for Information Extraction. Studia Linguistica Upsaliensia 17. 147 pp. Uppsala: Acta …

With the increasing availability of digitized resources of historical documents, interest in
effective natural language processing (NLP) for these documents is on the rise. However …

Does normalization help Part-of-Speech (POS) tagging accuracy on noisy, non-canonical
data? To the best of our knowledge, little is known on the actual impact of normalization in a …

This paper deals with normalization of language data from Early New High German. We
describe an unsupervised, rule-based approach which maps historical wordforms to modern …

Part-of-speech (PoS) tagging of non-standard language with models developed for standard
language is known to suffer from a significant decrease in accuracy. Two methods are …