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