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        <title>Normalising Non-standardised Orthography in Algerian Code-switched User-generated Data</title>
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    <name type="personal">
        <namePart type="given">Wafia</namePart>
        <namePart type="family">Adouane</namePart>
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    <name type="personal">
        <namePart type="given">Jean-Philippe</namePart>
        <namePart type="family">Bernardy</namePart>
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            <title>Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)</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>
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    <abstract>We work with Algerian, an under-resourced non-standardised Arabic variety, for which we compile a new parallel corpus consisting of user-generated textual data matched with normalised and corrected human annotations following data-driven and our linguistically motivated standard. We use an end-to-end deep neural model designed to deal with context-dependent spelling correction and normalisation. Results indicate that a model with two CNN sub-network encoders and an LSTM decoder performs the best, and that word context matters. Additionally, pre-processing data token-by-token with an edit-distance based aligner significantly improves the performance. We get promising results for the spelling correction and normalisation, as a pre-processing step for downstream tasks, on detecting binary Semantic Textual Similarity.</abstract>
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    <identifier type="doi">10.18653/v1/D19-5518</identifier>
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        <date>2019-11</date>
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%0 Conference Proceedings
%T Normalising Non-standardised Orthography in Algerian Code-switched User-generated Data
%A Adouane, Wafia
%A Bernardy, Jean-Philippe
%A Dobnik, Simon
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%Y Rahimi, Afshin
%S Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F adouane-etal-2019-normalising
%X We work with Algerian, an under-resourced non-standardised Arabic variety, for which we compile a new parallel corpus consisting of user-generated textual data matched with normalised and corrected human annotations following data-driven and our linguistically motivated standard. We use an end-to-end deep neural model designed to deal with context-dependent spelling correction and normalisation. Results indicate that a model with two CNN sub-network encoders and an LSTM decoder performs the best, and that word context matters. Additionally, pre-processing data token-by-token with an edit-distance based aligner significantly improves the performance. We get promising results for the spelling correction and normalisation, as a pre-processing step for downstream tasks, on detecting binary Semantic Textual Similarity.
%R 10.18653/v1/D19-5518
%U https://aclanthology.org/D19-5518
%U https://doi.org/10.18653/v1/D19-5518
%P 131-140

Markdown (Informal)

[Normalising Non-standardised Orthography in Algerian Code-switched User-generated Data](https://aclanthology.org/D19-5518) (Adouane et al., WNUT 2019)

• Normalising Non-standardised Orthography in Algerian Code-switched User-generated Data (Adouane et al., WNUT 2019)

ACL

• Wafia Adouane, Jean-Philippe Bernardy, and Simon Dobnik. 2019. Normalising Non-standardised Orthography in Algerian Code-switched User-generated Data. In Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019), pages 131–140, Hong Kong, China. Association for Computational Linguistics.