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        <title>Part-of-Speech and Morphological Tagging of Algerian Judeo-Arabic</title>
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            <title>Northern European Journal of Language Technology, Volume 8</title>
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    <abstract>Most linguistic studies of Judeo-Arabic, the ensemble of dialects spoken and written by Jews in Arab lands, are qualitative in nature and rely on laborious manual annotation work, and are therefore limited in scale. In this work, we develop automatic methods for morpho-syntactic tagging of Algerian Judeo-Arabic texts published by Algerian Jews in the 19th–20th centuries, based on a linguistically tagged corpus. First, we describe our semi-automatic approach for preprocessing these texts. Then, we experiment with both an off-the-shelf morphological tagger and several specially designed neural network taggers. Finally, we perform a real-world evaluation of new texts that were never tagged before in comparison with human expert annotators. Our experimental results demonstrate that these methods can dramatically speed up and improve the linguistic research pipeline, enabling linguists to study these dialects on a much greater scale.</abstract>
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    <identifier type="doi">https://doi.org/10.3384/nejlt.2000-1533.2022.4315</identifier>
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%0 Conference Proceedings
%T Part-of-Speech and Morphological Tagging of Algerian Judeo-Arabic
%A Tirosh-Becker, Ofra
%A Kessler, Michal
%A Becker, Oren
%A Belinkov, Yonatan
%Y Derczynski, Leon
%S Northern European Journal of Language Technology, Volume 8
%D 2022
%I Northern European Association of Language Technology
%C Copenhagen, Denmark
%F tirosh-becker-etal-2022-part
%X Most linguistic studies of Judeo-Arabic, the ensemble of dialects spoken and written by Jews in Arab lands, are qualitative in nature and rely on laborious manual annotation work, and are therefore limited in scale. In this work, we develop automatic methods for morpho-syntactic tagging of Algerian Judeo-Arabic texts published by Algerian Jews in the 19th–20th centuries, based on a linguistically tagged corpus. First, we describe our semi-automatic approach for preprocessing these texts. Then, we experiment with both an off-the-shelf morphological tagger and several specially designed neural network taggers. Finally, we perform a real-world evaluation of new texts that were never tagged before in comparison with human expert annotators. Our experimental results demonstrate that these methods can dramatically speed up and improve the linguistic research pipeline, enabling linguists to study these dialects on a much greater scale.
%R https://doi.org/10.3384/nejlt.2000-1533.2022.4315
%U https://aclanthology.org/2022.nejlt-1.7
%U https://doi.org/https://doi.org/10.3384/nejlt.2000-1533.2022.4315

Markdown (Informal)

[Part-of-Speech and Morphological Tagging of Algerian Judeo-Arabic](https://aclanthology.org/2022.nejlt-1.7) (Tirosh-Becker et al., NEJLT 2022)

• Part-of-Speech and Morphological Tagging of Algerian Judeo-Arabic (Tirosh-Becker et al., NEJLT 2022)

ACL

• Ofra Tirosh-Becker, Michal Kessler, Oren Becker, and Yonatan Belinkov. 2022. Part-of-Speech and Morphological Tagging of Algerian Judeo-Arabic. In Northern European Journal of Language Technology, Volume 8, Copenhagen, Denmark. Northern European Association of Language Technology.