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        <title>WiNER: A Wikipedia Annotated Corpus for Named Entity Recognition</title>
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    <name type="personal">
        <namePart type="given">Abbas</namePart>
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    <name type="personal">
        <namePart type="given">Phillippe</namePart>
        <namePart type="family">Langlais</namePart>
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            <title>Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)</title>
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            <namePart type="given">Greg</namePart>
            <namePart type="family">Kondrak</namePart>
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        <name type="personal">
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            <publisher>Asian Federation of Natural Language Processing</publisher>
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    <abstract>We revisit the idea of mining Wikipedia in order to generate named-entity annotations. We propose a new methodology that we applied to English Wikipedia to build WiNER, a large, high quality, annotated corpus. We evaluate its usefulness on 6 NER tasks, comparing 4 popular state-of-the art approaches. We show that LSTM-CRF is the approach that benefits the most from our corpus. We report impressive gains with this model when using a small portion of WiNER on top of the CONLL training material. Last, we propose a simple but efficient method for exploiting the full range of WiNER, leading to further improvements.</abstract>
    <identifier type="citekey">ghaddar-langlais-2017-winer</identifier>
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        <url>https://aclanthology.org/I17-1042</url>
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%0 Conference Proceedings
%T WiNER: A Wikipedia Annotated Corpus for Named Entity Recognition
%A Ghaddar, Abbas
%A Langlais, Phillippe
%Y Kondrak, Greg
%Y Watanabe, Taro
%S Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2017
%8 November
%I Asian Federation of Natural Language Processing
%C Taipei, Taiwan
%F ghaddar-langlais-2017-winer
%X We revisit the idea of mining Wikipedia in order to generate named-entity annotations. We propose a new methodology that we applied to English Wikipedia to build WiNER, a large, high quality, annotated corpus. We evaluate its usefulness on 6 NER tasks, comparing 4 popular state-of-the art approaches. We show that LSTM-CRF is the approach that benefits the most from our corpus. We report impressive gains with this model when using a small portion of WiNER on top of the CONLL training material. Last, we propose a simple but efficient method for exploiting the full range of WiNER, leading to further improvements.
%U https://aclanthology.org/I17-1042
%P 413-422

Markdown (Informal)

[WiNER: A Wikipedia Annotated Corpus for Named Entity Recognition](https://aclanthology.org/I17-1042) (Ghaddar & Langlais, IJCNLP 2017)

• WiNER: A Wikipedia Annotated Corpus for Named Entity Recognition (Ghaddar & Langlais, IJCNLP 2017)

ACL

• Abbas Ghaddar and Phillippe Langlais. 2017. WiNER: A Wikipedia Annotated Corpus for Named Entity Recognition. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 413–422, Taipei, Taiwan. Asian Federation of Natural Language Processing.