Conference Papers Year : 2022

Automatic Normalisation of Early Modern French

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.
Fichier principal
Vignette du fichier
LREC_2022_ModFr_Normalisation-29.pdf (839.84 Ko) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-03540226 , version 1 (23-01-2022)
hal-03540226 , version 2 (09-05-2022)

Identifiers

  • HAL Id : hal-03540226 , version 2

Cite

Rachel Bawden, Jonathan Poinhos, Eleni Kogkitsidou, Philippe Gambette, Benoît Sagot, et al.. Automatic Normalisation of Early Modern French. LREC 2022 - 13th Language Resources and Evaluation Conference, European Language Resources Association, Jun 2022, Marseille, France. pp.3354-3366. ⟨hal-03540226v2⟩
418 View
477 Download

Share

More