Add paper

README.md

@@ -12,3 +12,21 @@ pinned: false
 Synthetic trained spaCy pipelines for Latin NLP
 
 Developed by [Patrick J. Burns](https://diyclassics.github.io/), 2023.
+
+## Paper
+
+Details about training, datasets, etc. can be found in the following paper:
+Burns, P.J. 2023. “LatinCy: Synthetic Trained Pipelines for Latin NLP.” https://arxiv.org/abs/2305.04365v1.
+
+### Citation
+```
+@misc{burns_latincy_2023,
+	title = {{LatinCy}: Synthetic Trained Pipelines for Latin {NLP}},
+	author = {Burns, Patrick J.},
+	url = {https://arxiv.org/abs/2305.04365v1},
+	shorttitle = {{LatinCy}},
+	abstract = {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.},
+	date = {2023-05-07},
+	langid = {english},
+}
+```