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        <title>Data-driven Choices in Neural Part-of-Speech Tagging for Latin</title>
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        <namePart type="given">Geoff</namePart>
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            <title>Proceedings of LT4HALA 2020 - 1st Workshop on Language Technologies for Historical and Ancient Languages</title>
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        <name type="personal">
            <namePart type="given">Rachele</namePart>
            <namePart type="family">Sprugnoli</namePart>
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        <name type="personal">
            <namePart type="given">Marco</namePart>
            <namePart type="family">Passarotti</namePart>
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            <publisher>European Language Resources Association (ELRA)</publisher>
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    <abstract>Textual data in ancient and historical languages such as Latin is increasingly available in machine readable forms, yet computational tools to analyze and process this data are still lacking. We describe our system for part-of-speech tagging in Latin, an entry in the EvaLatin 2020 shared task. Based on a detailed analysis of the training data, we make targeted preprocessing decisions and design our model. We leverage existing large unlabelled resources to pre-train representations at both the grapheme and word level, which serve as the inputs to our LSTM-based models. We perform an extensive cross-validated hyperparameter search, achieving an accuracy score of up to 93 on in-domain texts. We publicly release all our code and trained models in the hope that our system will be of use to social scientists and digital humanists alike. The insights we draw from our inital analysis can also inform future NLP work modeling syntactic information in Latin.</abstract>
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%0 Conference Proceedings
%T Data-driven Choices in Neural Part-of-Speech Tagging for Latin
%A Bacon, Geoff
%Y Sprugnoli, Rachele
%Y Passarotti, Marco
%S Proceedings of LT4HALA 2020 - 1st Workshop on Language Technologies for Historical and Ancient Languages
%D 2020
%8 May
%I European Language Resources Association (ELRA)
%C Marseille, France
%@ 979-10-95546-53-5
%G English
%F bacon-2020-data
%X Textual data in ancient and historical languages such as Latin is increasingly available in machine readable forms, yet computational tools to analyze and process this data are still lacking. We describe our system for part-of-speech tagging in Latin, an entry in the EvaLatin 2020 shared task. Based on a detailed analysis of the training data, we make targeted preprocessing decisions and design our model. We leverage existing large unlabelled resources to pre-train representations at both the grapheme and word level, which serve as the inputs to our LSTM-based models. We perform an extensive cross-validated hyperparameter search, achieving an accuracy score of up to 93 on in-domain texts. We publicly release all our code and trained models in the hope that our system will be of use to social scientists and digital humanists alike. The insights we draw from our inital analysis can also inform future NLP work modeling syntactic information in Latin.
%U https://aclanthology.org/2020.lt4hala-1.17
%P 111-113

Markdown (Informal)

[Data-driven Choices in Neural Part-of-Speech Tagging for Latin](https://aclanthology.org/2020.lt4hala-1.17) (Bacon, LT4HALA 2020)

• Data-driven Choices in Neural Part-of-Speech Tagging for Latin (Bacon, LT4HALA 2020)

ACL

• Geoff Bacon. 2020. Data-driven Choices in Neural Part-of-Speech Tagging for Latin. In Proceedings of LT4HALA 2020 - 1st Workshop on Language Technologies for Historical and Ancient Languages, pages 111–113, Marseille, France. European Language Resources Association (ELRA).