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    <titleInfo>
        <title>ParsTwiNER: A Corpus for Named Entity Recognition at Informal Persian</title>
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    <name type="personal">
        <namePart type="given">MohammadMahdi</namePart>
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        <namePart type="given">AliAkbar</namePart>
        <namePart type="family">Badri</namePart>
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    <name type="personal">
        <namePart type="given">Hamid</namePart>
        <namePart type="family">Beigy</namePart>
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        <dateIssued>2021-11</dateIssued>
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            <title>Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)</title>
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            <namePart type="given">Wei</namePart>
            <namePart type="family">Xu</namePart>
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        <name type="personal">
            <namePart type="given">Alan</namePart>
            <namePart type="family">Ritter</namePart>
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    <abstract>As a result of unstructured sentences and some misspellings and errors, finding named entities in a noisy environment such as social media takes much more effort. ParsTwiNER contains about 250k tokens, based on standard instructions like MUC-6 or CoNLL 2003, gathered from Persian Twitter. Using Cohen’s Kappa coefficient, the consistency of annotators is 0.95, a high score. In this study, we demonstrate that some state-of-the-art models degrade on these corpora, and trained a new model using parallel transfer learning based on the BERT architecture. Experimental results show that the model works well in informal Persian as well as in formal Persian.</abstract>
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    <identifier type="doi">10.18653/v1/2021.wnut-1.16</identifier>
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        <date>2021-11</date>
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            <start>131</start>
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%0 Conference Proceedings
%T ParsTwiNER: A Corpus for Named Entity Recognition at Informal Persian
%A Aghajani, MohammadMahdi
%A Badri, AliAkbar
%A Beigy, Hamid
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%Y Rahimi, Afshin
%S Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online
%F aghajani-etal-2021-parstwiner
%X As a result of unstructured sentences and some misspellings and errors, finding named entities in a noisy environment such as social media takes much more effort. ParsTwiNER contains about 250k tokens, based on standard instructions like MUC-6 or CoNLL 2003, gathered from Persian Twitter. Using Cohen’s Kappa coefficient, the consistency of annotators is 0.95, a high score. In this study, we demonstrate that some state-of-the-art models degrade on these corpora, and trained a new model using parallel transfer learning based on the BERT architecture. Experimental results show that the model works well in informal Persian as well as in formal Persian.
%R 10.18653/v1/2021.wnut-1.16
%U https://aclanthology.org/2021.wnut-1.16
%U https://doi.org/10.18653/v1/2021.wnut-1.16
%P 131-136

Markdown (Informal)

[ParsTwiNER: A Corpus for Named Entity Recognition at Informal Persian](https://aclanthology.org/2021.wnut-1.16) (Aghajani et al., WNUT 2021)

• ParsTwiNER: A Corpus for Named Entity Recognition at Informal Persian (Aghajani et al., WNUT 2021)

ACL

• MohammadMahdi Aghajani, AliAkbar Badri, and Hamid Beigy. 2021. ParsTwiNER: A Corpus for Named Entity Recognition at Informal Persian. In Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021), pages 131–136, Online. Association for Computational Linguistics.