Tag your Modern French text
You can find more information on this specific model here: https://arxiv.org/abs/2005.07505
You can find more information on this specific model here: https://arxiv.org/abs/2005.07505
The model is trained on transcriptions with modernised spelling.
The model was trained on the following corpora :
The annotations are made according to the following reference lists:
More information on the annotation practice can be found in Simon Gabay, Jean-Baptiste Camps, Thibault Clérice, Manuel d'annotation linguistique pour le français moderne (XVIe -XVIIIe siècles) 2020: https://hal.archives-ouvertes.fr/hal-02571190.
Sample from annotation:
token lemma POS morph treated
Il il PROimp PERS.=3|NOMB.=s|GENRE=m|CAS=n Il
faut falloir VERcjg MODE=ind|TEMPS=pst|PERS.=3|NOMB.=s faut
que que CONsub MORPH=empty que
ce ce DETdem NOMB.=s|GENRE=m ce
matin matin NOMcom NOMB.=s|GENRE=m matin
, , PONfbl MORPH=empty ,
à à PRE MORPH=empty à
force force NOMcom NOMB.=s|GENRE=f force
de de PRE MORPH=empty de
trop trop ADVgen MORPH=empty trop
boire boire VERinf MORPH=empty boire
, , PONfbl MORPH=empty ,
Il il PROper PERS.=3|NOMB.=s|GENRE=m|CAS=n Il
se se PROper PERS.=3|NOMB.=s|CAS=r se
soit être VERcjg MODE=sub|TEMPS=pst|PERS.=3|NOMB.=s soit
troublé troubler VERppe NOMB.=s|GENRE=m troublé
le le DETdef NOMB.=s|GENRE=m le
cerveau cerveau NOMcom NOMB.=s|GENRE=m cerveau
. . PONfrt MORPH=empty .
Please remember that corpus creation and software engineering is valid research, so please cite these resources when you use this lemmatizer for your research: this includes the wonderful original research by E. Manjavacas, M. Kestemont and Á. Kádár as well as the software wrapping built to handle pre- and post-processing.
For each models, a bibliography and potentially other citable works are given, such as models and datasets are given.
@software{thibault_clerice_2020_3883590,
author = {Clérice, Thibault},
title = {Pie Extended, an extension for Pie with pre-processing and post-processing},
month = jun,
year = 2020,
publisher = {Zenodo},
doi = {10.5281/zenodo.3883589},
url = {https://doi.org/10.5281/zenodo.3883589}
}
@inproceedings{manjavacas-etal-2019-improving,
title = "Improving Lemmatization of Non-Standard Languages with Joint Learning",
author = "Manjavacas, Enrique and
K{\'a}d{\'a}r, {\'A}kos and
Kestemont, Mike",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational
Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/N19-1153",
doi = "10.18653/v1/N19-1153",
pages = "1493--1503",}
@misc{camps2020corpus,
title={Corpus and Models for Lemmatisation and POS-tagging of Classical French Theatre},
author={Jean-Baptiste Camps and
Simon Gabay and
Paul Fièvre and
Thibault Clérice and
Florian Cafiero},
year={2020},
eprint={2005.07505},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2005.07505},
primaryClass={cs.CL}
}
This lemmatizer is provided to you thanks to the data of the LASLA, the software of Emmanuel Manjavacas and Mike Kestemont and some engineering from the École nationale des chartes. If you want to cite them :