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        <title>Lemmatization and POS-tagging process by using joint learning approach. Experimental results on Classical Armenian, Old Georgian, and Syriac</title>
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        <namePart type="given">Chahan</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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            <namePart type="family">Sprugnoli</namePart>
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    <abstract>Classical Armenian, Old Georgian and Syriac are under-resourced digital languages. Even though a lot of printed critical editions or dictionaries are available, there is currently a lack of fully tagged corpora that could be reused for automatic text analysis. In this paper, we introduce an ongoing project of lemmatization and POS-tagging for these languages, relying on a recurrent neural network (RNN), specific morphological tags and dedicated datasets. For this paper, we have combine different corpora previously processed by automatic out-of-context lemmatization and POS-tagging, and manual proofreading by the collaborators of the GREgORI Project (UCLouvain, Louvain-la-Neuve, Belgium). We intend to compare a rule based approach and a RNN approach by using PIE specialized by Calfa (Paris, France). We introduce here first results. We reach a mean accuracy of 91,63% in lemmatization and of 92,56% in POS-tagging. The datasets, which were constituted and used for this project, are not yet representative of the different variations of these languages through centuries, but they are homogenous and allow reaching tangible results, paving the way for further analysis of wider corpora.</abstract>
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%0 Conference Proceedings
%T Lemmatization and POS-tagging process by using joint learning approach. Experimental results on Classical Armenian, Old Georgian, and Syriac
%A Vidal-Gorène, Chahan
%A Kindt, Bastien
%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 vidal-gorene-kindt-2020-lemmatization
%X Classical Armenian, Old Georgian and Syriac are under-resourced digital languages. Even though a lot of printed critical editions or dictionaries are available, there is currently a lack of fully tagged corpora that could be reused for automatic text analysis. In this paper, we introduce an ongoing project of lemmatization and POS-tagging for these languages, relying on a recurrent neural network (RNN), specific morphological tags and dedicated datasets. For this paper, we have combine different corpora previously processed by automatic out-of-context lemmatization and POS-tagging, and manual proofreading by the collaborators of the GREgORI Project (UCLouvain, Louvain-la-Neuve, Belgium). We intend to compare a rule based approach and a RNN approach by using PIE specialized by Calfa (Paris, France). We introduce here first results. We reach a mean accuracy of 91,63% in lemmatization and of 92,56% in POS-tagging. The datasets, which were constituted and used for this project, are not yet representative of the different variations of these languages through centuries, but they are homogenous and allow reaching tangible results, paving the way for further analysis of wider corpora.
%U https://aclanthology.org/2020.lt4hala-1.4
%P 22-27

Markdown (Informal)

[Lemmatization and POS-tagging process by using joint learning approach. Experimental results on Classical Armenian, Old Georgian, and Syriac](https://aclanthology.org/2020.lt4hala-1.4) (Vidal-Gorène & Kindt, LT4HALA 2020)

• Lemmatization and POS-tagging process by using joint learning approach. Experimental results on Classical Armenian, Old Georgian, and Syriac (Vidal-Gorène & Kindt, LT4HALA 2020)

ACL

• Chahan Vidal-Gorène and Bastien Kindt. 2020. Lemmatization and POS-tagging process by using joint learning approach. Experimental results on Classical Armenian, Old Georgian, and Syriac. In Proceedings of LT4HALA 2020 - 1st Workshop on Language Technologies for Historical and Ancient Languages, pages 22–27, Marseille, France. European Language Resources Association (ELRA).