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        <title>A Multi-task Approach for Named Entity Recognition in Social Media Data</title>
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    <name type="personal">
        <namePart type="given">Gustavo</namePart>
        <namePart type="family">Aguilar</namePart>
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        <namePart type="given">Suraj</namePart>
        <namePart type="family">Maharjan</namePart>
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    <name type="personal">
        <namePart type="given">Adrian</namePart>
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    <name type="personal">
        <namePart type="given">Thamar</namePart>
        <namePart type="family">Solorio</namePart>
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            <title>Proceedings of the 3rd Workshop on Noisy User-generated Text</title>
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        <name type="personal">
            <namePart type="given">Leon</namePart>
            <namePart type="family">Derczynski</namePart>
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        <name type="personal">
            <namePart type="given">Wei</namePart>
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        <name type="personal">
            <namePart type="given">Alan</namePart>
            <namePart type="family">Ritter</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Tim</namePart>
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    <abstract>Named Entity Recognition for social media data is challenging because of its inherent noisiness. In addition to improper grammatical structures, it contains spelling inconsistencies and numerous informal abbreviations. We propose a novel multi-task approach by employing a more general secondary task of Named Entity (NE) segmentation together with the primary task of fine-grained NE categorization. The multi-task neural network architecture learns higher order feature representations from word and character sequences along with basic Part-of-Speech tags and gazetteer information. This neural network acts as a feature extractor to feed a Conditional Random Fields classifier. We were able to obtain the first position in the 3rd Workshop on Noisy User-generated Text (WNUT-2017) with a 41.86% entity F1-score and a 40.24% surface F1-score.</abstract>
    <identifier type="citekey">aguilar-etal-2017-multi</identifier>
    <identifier type="doi">10.18653/v1/W17-4419</identifier>
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        <date>2017-09</date>
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%0 Conference Proceedings
%T A Multi-task Approach for Named Entity Recognition in Social Media Data
%A Aguilar, Gustavo
%A Maharjan, Suraj
%A López-Monroy, Adrian Pastor
%A Solorio, Thamar
%Y Derczynski, Leon
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%S Proceedings of the 3rd Workshop on Noisy User-generated Text
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F aguilar-etal-2017-multi
%X Named Entity Recognition for social media data is challenging because of its inherent noisiness. In addition to improper grammatical structures, it contains spelling inconsistencies and numerous informal abbreviations. We propose a novel multi-task approach by employing a more general secondary task of Named Entity (NE) segmentation together with the primary task of fine-grained NE categorization. The multi-task neural network architecture learns higher order feature representations from word and character sequences along with basic Part-of-Speech tags and gazetteer information. This neural network acts as a feature extractor to feed a Conditional Random Fields classifier. We were able to obtain the first position in the 3rd Workshop on Noisy User-generated Text (WNUT-2017) with a 41.86% entity F1-score and a 40.24% surface F1-score.
%R 10.18653/v1/W17-4419
%U https://aclanthology.org/W17-4419/
%U https://doi.org/10.18653/v1/W17-4419
%P 148-153

Markdown (Informal)

[A Multi-task Approach for Named Entity Recognition in Social Media Data](https://aclanthology.org/W17-4419/) (Aguilar et al., WNUT 2017)

• A Multi-task Approach for Named Entity Recognition in Social Media Data (Aguilar et al., WNUT 2017)

ACL

• Gustavo Aguilar, Suraj Maharjan, Adrian Pastor López-Monroy, and Thamar Solorio. 2017. A Multi-task Approach for Named Entity Recognition in Social Media Data. In Proceedings of the 3rd Workshop on Noisy User-generated Text, pages 148–153, Copenhagen, Denmark. Association for Computational Linguistics.