Automatic Normalisation of Early Modern French

Rachel Bawden, Jonathan Poinhos, Eleni Kogkitsidou, Philippe Gambette, Benoît Sagot, Simon Gabay


Abstract
Spelling normalisation is a useful step in the study and analysis of historical language texts, whether it is manual analysis by experts or automatic analysis using downstream natural language processing (NLP) tools. Not only does it help to homogenise the variable spelling that often exists in historical texts, but it also facilitates the use of off-the-shelf contemporary NLP tools, if contemporary spelling conventions are used for normalisation. We present FREEMnorm, a new benchmark for the normalisation of Early Modern French (from the 17th century) into contemporary French and provide a thorough comparison of three different normalisation methods: ABA, an alignment-based approach and MT-approaches, (both statistical and neural), including extensive parameter searching, which is often missing in the normalisation literature.
Anthology ID:
2022.lrec-1.358
Volume:
Proceedings of the Thirteenth Language Resources and Evaluation Conference
Month:
June
Year:
2022
Address:
Marseille, France
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
3354–3366
Language:
URL:
https://aclanthology.org/2022.lrec-1.358
DOI:
Bibkey:
Cite (ACL):
Rachel Bawden, Jonathan Poinhos, Eleni Kogkitsidou, Philippe Gambette, Benoît Sagot, and Simon Gabay. 2022. Automatic Normalisation of Early Modern French. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 3354–3366, Marseille, France. European Language Resources Association.
Cite (Informal):
Automatic Normalisation of Early Modern French (Bawden et al., LREC 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.lrec-1.358.pdf
Code
 rbawden/modfr-norm