LatinCy: Synthetic Trained Pipelines for Latin NLP
This provides a practical tool for Latin-language researchers, though it is incremental as it applies existing methods to new data.
The paper tackles the lack of general-purpose NLP tools for Latin by introducing LatinCy, a set of trained pipelines for the spaCy framework, achieving high accuracies such as 97.41% for POS tagging and 94.66% for lemmatization.
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.