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        <title>Learning to recognise named entities in tweets by exploiting weakly labelled data</title>
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        <namePart type="given">Kurt</namePart>
        <namePart type="given">Junshean</namePart>
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    <name type="personal">
        <namePart type="given">Riza</namePart>
        <namePart type="given">Theresa</namePart>
        <namePart type="family">Batista-Navarro</namePart>
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    <name type="personal">
        <namePart type="given">Sophia</namePart>
        <namePart type="family">Ananiadou</namePart>
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            <title>Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT)</title>
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            <namePart type="given">Bo</namePart>
            <namePart type="family">Han</namePart>
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        <name type="personal">
            <namePart type="given">Alan</namePart>
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            <namePart type="given">Tim</namePart>
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            <publisher>The COLING 2016 Organizing Committee</publisher>
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    <abstract>Named entity recognition (NER) in social media (e.g., Twitter) is a challenging task due to the noisy nature of text. As part of our participation in the W-NUT 2016 Named Entity Recognition Shared Task, we proposed an unsupervised learning approach using deep neural networks and leverage a knowledge base (i.e., DBpedia) to bootstrap sparse entity types with weakly labelled data. To further boost the performance, we employed a more sophisticated tagging scheme and applied dropout as a regularisation technique in order to reduce overfitting. Even without hand-crafting linguistic features nor leveraging any of the W-NUT-provided gazetteers, we obtained robust performance with our approach, which ranked third amongst all shared task participants according to the official evaluation on a gold standard named entity-annotated corpus of 3,856 tweets.</abstract>
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        <url>https://aclanthology.org/W16-3921</url>
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%0 Conference Proceedings
%T Learning to recognise named entities in tweets by exploiting weakly labelled data
%A Espinosa, Kurt Junshean
%A Batista-Navarro, Riza Theresa
%A Ananiadou, Sophia
%Y Han, Bo
%Y Ritter, Alan
%Y Derczynski, Leon
%Y Xu, Wei
%Y Baldwin, Tim
%S Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT)
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F espinosa-etal-2016-learning
%X Named entity recognition (NER) in social media (e.g., Twitter) is a challenging task due to the noisy nature of text. As part of our participation in the W-NUT 2016 Named Entity Recognition Shared Task, we proposed an unsupervised learning approach using deep neural networks and leverage a knowledge base (i.e., DBpedia) to bootstrap sparse entity types with weakly labelled data. To further boost the performance, we employed a more sophisticated tagging scheme and applied dropout as a regularisation technique in order to reduce overfitting. Even without hand-crafting linguistic features nor leveraging any of the W-NUT-provided gazetteers, we obtained robust performance with our approach, which ranked third amongst all shared task participants according to the official evaluation on a gold standard named entity-annotated corpus of 3,856 tweets.
%U https://aclanthology.org/W16-3921
%P 153-163

Markdown (Informal)

[Learning to recognise named entities in tweets by exploiting weakly labelled data](https://aclanthology.org/W16-3921) (Espinosa et al., WNUT 2016)

• Learning to recognise named entities in tweets by exploiting weakly labelled data (Espinosa et al., WNUT 2016)

ACL

• Kurt Junshean Espinosa, Riza Theresa Batista-Navarro, and Sophia Ananiadou. 2016. Learning to recognise named entities in tweets by exploiting weakly labelled data. In Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT), pages 153–163, Osaka, Japan. The COLING 2016 Organizing Committee.