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    <titleInfo>
        <title>Bidirectional LSTM for Named Entity Recognition in Twitter Messages</title>
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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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            <namePart type="given">Alan</namePart>
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            <namePart type="given">Leon</namePart>
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    <abstract>In this paper, we present our approach for named entity recognition in Twitter messages that we used in our participation in the Named Entity Recognition in Twitter shared task at the COLING 2016 Workshop on Noisy User-generated text (WNUT). The main challenge that we aim to tackle in our participation is the short, noisy and colloquial nature of tweets, which makes named entity recognition in Twitter message a challenging task. In particular, we investigate an approach for dealing with this problem by enabling bidirectional long short-term memory (LSTM) to automatically learn orthographic features without requiring feature engineering. In comparison with other systems participating in the shared task, our system achieved the most effective performance on both the ‘segmentation and categorisation’ and the ‘segmentation only’ sub-tasks.</abstract>
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%0 Conference Proceedings
%T Bidirectional LSTM for Named Entity Recognition in Twitter Messages
%A Limsopatham, Nut
%A Collier, Nigel
%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 limsopatham-collier-2016-bidirectional
%X In this paper, we present our approach for named entity recognition in Twitter messages that we used in our participation in the Named Entity Recognition in Twitter shared task at the COLING 2016 Workshop on Noisy User-generated text (WNUT). The main challenge that we aim to tackle in our participation is the short, noisy and colloquial nature of tweets, which makes named entity recognition in Twitter message a challenging task. In particular, we investigate an approach for dealing with this problem by enabling bidirectional long short-term memory (LSTM) to automatically learn orthographic features without requiring feature engineering. In comparison with other systems participating in the shared task, our system achieved the most effective performance on both the ‘segmentation and categorisation’ and the ‘segmentation only’ sub-tasks.
%U https://aclanthology.org/W16-3920
%P 145-152

Markdown (Informal)

[Bidirectional LSTM for Named Entity Recognition in Twitter Messages](https://aclanthology.org/W16-3920) (Limsopatham & Collier, WNUT 2016)

• Bidirectional LSTM for Named Entity Recognition in Twitter Messages (Limsopatham & Collier, WNUT 2016)

ACL

• Nut Limsopatham and Nigel Collier. 2016. Bidirectional LSTM for Named Entity Recognition in Twitter Messages. In Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT), pages 145–152, Osaka, Japan. The COLING 2016 Organizing Committee.