Teklia/spacy-home-alcar-person

Spacy - HOME-Alcar - Person

This model detects Person entities in Latin.

Model description

The model has been trained using the Spacy v2 library on the HOME-Alcar document annotations to detect the person and location entities. The model is compatible with version 2.3.5 of Spacy and incompatible with versions 3.x.x.

Evaluation results

The model achieves the following results on HOME-Alcar:

tag	predicted	matched	Precision	Recall	F1	Support
PERS	28,276	28,006	0.990	0.997	0.994	28,087
LOC	17,627	17,406	0.987	0.983	0.985	17,699
All	45,903	45,412	0.989	0.992	0.991	45,786

How to use?

Please refer to the Spacy library page (https://pypi.org/project/spacy/2.3.5/) to use this model.

Cite us!

@inproceedings{10.1007/978-3-031-06555-2_29,
    author = {Monroc, Claire Bizon and Miret, Blanche and Bonhomme, Marie-Laurence and Kermorvant, Christopher},
    title = {A Comprehensive Study Of Open-Source Libraries For Named Entity Recognition On Handwritten Historical Documents},
    year = {2022},
    isbn = {978-3-031-06554-5},
    publisher = {Springer-Verlag},
    address = {Berlin, Heidelberg},
    url = {https://doi.org/10.1007/978-3-031-06555-2_29},
    doi = {10.1007/978-3-031-06555-2_29},
    abstract = {In this paper, we propose an evaluation of several state-of-the-art open-source natural language processing (NLP) libraries for named entity recognition (NER) on handwritten historical documents: spaCy, Stanza and Flair. The comparison is carried out on three low-resource multilingual datasets of handwritten historical documents: HOME (a multilingual corpus of medieval charters), Balsac (a corpus of parish records from Quebec), and Esposalles (a corpus of marriage records in Catalan). We study the impact of the document recognition processes (text line detection and handwriting recognition) on the performance of the NER. We show that current off-the-shelf NER libraries yield state-of-the-art results, even on low-resource languages or multilingual documents using multilingual models. We show, in an end-to-end evaluation, that text line detection errors have a greater impact than handwriting recognition errors. Finally, we also report state-of-the-art results on the public Esposalles dataset.},
    booktitle = {Document Analysis Systems: 15th IAPR International Workshop, DAS 2022, La Rochelle, France, May 22–25, 2022, Proceedings},
    pages = {429–444},
    numpages = {16},
    keywords = {Text line detection, Named entity recognition, Handwritten historical documents},
    location = {La Rochelle, France}
}