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    <titleInfo>
        <title>Robust Lexical Features for Improved Neural Network Named-Entity Recognition</title>
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    <name type="personal">
        <namePart type="given">Abbas</namePart>
        <namePart type="family">Ghaddar</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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    <abstract>Neural network approaches to Named-Entity Recognition reduce the need for carefully hand-crafted features. While some features do remain in state-of-the-art systems, lexical features have been mostly discarded, with the exception of gazetteers. In this work, we show that this is unfair: lexical features are actually quite useful. We propose to embed words and entity types into a low-dimensional vector space we train from annotated data produced by distant supervision thanks to Wikipedia. From this, we compute — offline — a feature vector representing each word. When used with a vanilla recurrent neural network model, this representation yields substantial improvements. We establish a new state-of-the-art F1 score of 87.95 on ONTONOTES 5.0, while matching state-of-the-art performance with a F1 score of 91.73 on the over-studied CONLL-2003 dataset.</abstract>
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        <date>2018-08</date>
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            <start>1896</start>
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%0 Conference Proceedings
%T Robust Lexical Features for Improved Neural Network Named-Entity Recognition
%A Ghaddar, Abbas
%A Langlais, Phillippe
%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 ghaddar-langlais-2018-robust
%X Neural network approaches to Named-Entity Recognition reduce the need for carefully hand-crafted features. While some features do remain in state-of-the-art systems, lexical features have been mostly discarded, with the exception of gazetteers. In this work, we show that this is unfair: lexical features are actually quite useful. We propose to embed words and entity types into a low-dimensional vector space we train from annotated data produced by distant supervision thanks to Wikipedia. From this, we compute — offline — a feature vector representing each word. When used with a vanilla recurrent neural network model, this representation yields substantial improvements. We establish a new state-of-the-art F1 score of 87.95 on ONTONOTES 5.0, while matching state-of-the-art performance with a F1 score of 91.73 on the over-studied CONLL-2003 dataset.
%U https://aclanthology.org/C18-1161
%P 1896-1907

Markdown (Informal)

[Robust Lexical Features for Improved Neural Network Named-Entity Recognition](https://aclanthology.org/C18-1161) (Ghaddar & Langlais, COLING 2018)

• Robust Lexical Features for Improved Neural Network Named-Entity Recognition (Ghaddar & Langlais, COLING 2018)

ACL

• Abbas Ghaddar and Phillippe Langlais. 2018. Robust Lexical Features for Improved Neural Network Named-Entity Recognition. In Proceedings of the 27th International Conference on Computational Linguistics, pages 1896–1907, Santa Fe, New Mexico, USA. Association for Computational Linguistics.