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  5. Review: Language and Chronology: Text Dating by Machine Learning (Toner and Han)
 
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Review: Language and Chronology: Text Dating by Machine Learning (Toner and Han)

Alternative Title
Language and Chronology: Text Dating by Machine Learning. Gregory Toner and Xiwu Han. Language and Computers 84. Brill, Leiden/Boston, 2019. xii + 183pp.€88. ISBN 978-90-04-41003-9
Author(s)
Qiu, Fangzhe  
Uri
http://hdl.handle.net/10197/12243
Date Issued
2020
Date Available
2021-06-17T14:26:27Z
Abstract
In 2015, two grants were awarded for projects using computational and statistical methods to date medieval Irish texts: one is provided by the European Research Council, which funds the project Chronologicon Hibernicum in Maynooth; the other is awarded by the Leverhulme Trust to fund Gregory Toner’s project ‘Dating of medieval texts through regressive analysis of the lexicon’ in QUB. The present book is the outcome of the latter project, in which the two co– authors explore computational methods previously unknown to the field of medieval Irish studies and demonstrate the huge potential such methods embody for the discipline. When one compares the title of the project to that of the book, it is apparent that Toner and Han have pushed the methodological boundary much further by transcending from regression analysis in the initial project idea to advanced machine learning techniques in the outcome.
Type of Material
Review
Publisher
School of Celtic Studies, Dublin Institute for Advanced Studies
Journal
Celtica
Volume
32
Start Page
265
End Page
272
Subjects

Machine learning

Text corpora

Medieval manuscripts

Irish language

Digital humanities

Web versions
https://www.dias.ie/celt/celtica/celtica-contents/celtica-volume-32-2020/
Language
English
Status of Item
Peer reviewed
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
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Owning collection
Irish, Celtic Studies and Folklore Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
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