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        <title>A Survey on Recent Advances in Named Entity Recognition from Deep Learning models</title>
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    <name type="personal">
        <namePart type="given">Vikas</namePart>
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            <title>Proceedings of the 27th International Conference on Computational Linguistics</title>
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            <namePart type="given">Emily</namePart>
            <namePart type="given">M</namePart>
            <namePart type="family">Bender</namePart>
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            <namePart type="given">Leon</namePart>
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            <namePart type="given">Pierre</namePart>
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    <abstract>Named Entity Recognition (NER) is a key component in NLP systems for question answering, information retrieval, relation extraction, etc. NER systems have been studied and developed widely for decades, but accurate systems using deep neural networks (NN) have only been introduced in the last few years. We present a comprehensive survey of deep neural network architectures for NER, and contrast them with previous approaches to NER based on feature engineering and other supervised or semi-supervised learning algorithms. Our results highlight the improvements achieved by neural networks, and show how incorporating some of the lessons learned from past work on feature-based NER systems can yield further improvements.</abstract>
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%0 Conference Proceedings
%T A Survey on Recent Advances in Named Entity Recognition from Deep Learning models
%A Yadav, Vikas
%A Bethard, Steven
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F yadav-bethard-2018-survey
%X Named Entity Recognition (NER) is a key component in NLP systems for question answering, information retrieval, relation extraction, etc. NER systems have been studied and developed widely for decades, but accurate systems using deep neural networks (NN) have only been introduced in the last few years. We present a comprehensive survey of deep neural network architectures for NER, and contrast them with previous approaches to NER based on feature engineering and other supervised or semi-supervised learning algorithms. Our results highlight the improvements achieved by neural networks, and show how incorporating some of the lessons learned from past work on feature-based NER systems can yield further improvements.
%U https://aclanthology.org/C18-1182
%P 2145-2158

Markdown (Informal)

[A Survey on Recent Advances in Named Entity Recognition from Deep Learning models](https://aclanthology.org/C18-1182) (Yadav & Bethard, COLING 2018)

• A Survey on Recent Advances in Named Entity Recognition from Deep Learning models (Yadav & Bethard, COLING 2018)

ACL

• Vikas Yadav and Steven Bethard. 2018. A Survey on Recent Advances in Named Entity Recognition from Deep Learning models. In Proceedings of the 27th International Conference on Computational Linguistics, pages 2145–2158, Santa Fe, New Mexico, USA. Association for Computational Linguistics.