The transformer has been shown to outperform recurrent neural network-based sequence-to-
sequence models in various word-level NLP tasks. Yet for character-level transduction tasks …

There is no consensus on the state-of-the-art approach to historical text normalization. Many
techniques have been proposed, including rule-based methods, distance metrics, character …

Natural-language processing of historical documents is complicated by the abundance of
variant spellings and lack of annotated data. A common approach is to normalize the …

Text normalization is the task of mapping non-canonical language, typical of speech
transcription and computer-mediated communication, to a standardized writing. It is an up …

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 …

The paper presents two manually annotated Slovene language text normalisation datasets,
one of historical texts and the other of tweets, and proposes several variants of character …

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 …

Automated processing of historical texts often relies on pre-normalization to modern word
forms. Training encoder-decoder architectures to solve such problems typically requires a lot …

We propose a language-independent word normalization method exemplified on
modernizing historical Slovene words. Our method relies on character-based statistical …

With the increasing availability of digitized resources of historical documents, interest in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 …