LatinCy: Synthetic Trained Pipelines for Latin NLP
Patrick J. Burns
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.