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Erschienen in:
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2021 | OriginalPaper | Buchkapitel

A Comparison of Character-Based Neural Machine Translations Techniques Applied to Spelling Normalization

verfasst von : Miguel Domingo, Francisco Casacuberta

Erschienen in: Pattern Recognition. ICPR International Workshops and Challenges

Verlag: Springer International Publishing

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Abstract

The lack of spelling conventions and the natural evolution of human language create a linguistic barrier inherent in historical documents. This barrier has always been a concern for scholars in humanities. In order to tackle this problem, spelling normalization aims to adapt a document’s orthography to modern standards. In this work, we evaluate several character-based neural machine translation normalization approaches—using modern documents to enrich the neural models. We evaluated these approaches on several datasets from different languages and time periods, reaching the conclusion that each approach is better suited for a different set of documents.

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Vorheriges Kapitel A Contextual Approach for Coastal Tourism and Cultural Heritage Enhancing
Nächstes Kapitel Weakly Supervised Bounding Box Extraction for Unlabeled Data in Table Detection
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Metadaten
Titel
A Comparison of Character-Based Neural Machine Translations Techniques Applied to Spelling Normalization
verfasst von
Miguel Domingo
Francisco Casacuberta
Copyright-Jahr
2021
Buch
Pattern Recognition. ICPR International Workshops and Challenges

Print ISBN: 978-3-030-68786-1

Electronic ISBN: 978-3-030-68787-8

Copyright-Jahr: 2021

DOI
https://doi.org/10.1007/978-3-030-68787-8_24