LatinCy: Synthetic Trained Pipelines for Latin NLP

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3A8ILUKGMT" target="_blank" >RIV/00216208:11320/23:8ILUKGMT - isvavai.cz</a>

Výsledek na webu

<a href="http://arxiv.org/abs/2305.04365" target="_blank" >http://arxiv.org/abs/2305.04365</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.48550/arXiv.2305.04365" target="_blank" >10.48550/arXiv.2305.04365</a>

Jazyk výsledku

angličtina

Název v původním jazyce

LatinCy: Synthetic Trained Pipelines for Latin NLP

Popis výsledku v původním jazyce

"This paper introduces LatinCy, a set of trained general purpose Latin-language "core" pipelines for use with the spaCy natural language processing framework. The models are trained on a large amount of available Latin data, including all five of the Latin Universal Dependency treebanks, which have been preprocessed to be compatible with each other. The result is a set of general models for Latin with good performance on a number of natural language processing tasks (e.g. the top-performing model yields POS tagging, 97.41% accuracy; lemmatization, 94.66% accuracy; morphological tagging 92.76% accuracy). The paper describes the model training, including its training data and parameterization, and presents the advantages to Latin-language researchers of having a spaCy model available for NLP work."

Název v anglickém jazyce

LatinCy: Synthetic Trained Pipelines for Latin NLP

Popis výsledku anglicky

"This paper introduces LatinCy, a set of trained general purpose Latin-language "core" pipelines for use with the spaCy natural language processing framework. The models are trained on a large amount of available Latin data, including all five of the Latin Universal Dependency treebanks, which have been preprocessed to be compatible with each other. The result is a set of general models for Latin with good performance on a number of natural language processing tasks (e.g. the top-performing model yields POS tagging, 97.41% accuracy; lemmatization, 94.66% accuracy; morphological tagging 92.76% accuracy). The paper describes the model training, including its training data and parameterization, and presents the advantages to Latin-language researchers of having a spaCy model available for NLP work."

Druh

O - Ostatní výsledky

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

—

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů