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
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 3354–3366
- Language:
- URL:
- https://aclanthology.org/2022.lrec-1.358
- DOI:
- 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)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/2022.lrec-1.358.pdf
- Code
- rbawden/modfr-norm