Tag your Middle Dutch text
You can find more information on this specific model here: https://github.com/hipster-philology/middle-dutch-model
You can find more information on this specific model here: https://github.com/hipster-philology/middle-dutch-model
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",}
@article{KestemontEA17,
author = {Mike Kestemont and
Guy De Pauw and
Renske van Nie and
Walter Daelemans},
title = {Lemmatization for variation-rich languages using deep learning},
journal = {Digital Scholarship in the Humanities},
volume = {32},
number = {4},
pages = {797--815},
year = {2017},
url = {https://doi.org/10.1093/llc/fqw034},
doi = {10.1093/llc/fqw034},
}
This model provides support for the lemmatization and part-of-speech tagging of Middle Dutch texts (ca. 1150-1450 AD). The model was trained on the union of the four main corpora that are available for Middle Dutch dialects. An attempt has been made at uniformizing the various tagging conventions across the subcorpora, but this model generally aims for a maximal lexical coverage, at the expense of some consistency in tagging. The model furthermore assumes that your input has been tokenized already: although it can deal with clitical forms (e.g.
Much credit should go to the Dutch Language Institute who are the primary curator of these materials.
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 :