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    <titleInfo>
        <title>On the Strength of Character Language Models for Multilingual Named Entity Recognition</title>
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    <name type="personal">
        <namePart type="given">Xiaodong</namePart>
        <namePart type="family">Yu</namePart>
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    <name type="personal">
        <namePart type="given">Stephen</namePart>
        <namePart type="family">Mayhew</namePart>
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        <namePart type="given">Dan</namePart>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Ellen</namePart>
            <namePart type="family">Riloff</namePart>
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            <namePart type="given">David</namePart>
            <namePart type="family">Chiang</namePart>
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            <namePart type="family">Hockenmaier</namePart>
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            <namePart type="given">Jun’ichi</namePart>
            <namePart type="family">Tsujii</namePart>
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    <abstract>Character-level patterns have been widely used as features in English Named Entity Recognition (NER) systems. However, to date there has been no direct investigation of the inherent differences between name and nonname tokens in text, nor whether this property holds across multiple languages. This paper analyzes the capabilities of corpus-agnostic Character-level Language Models (CLMs) in the binary task of distinguishing name tokens from non-name tokens. We demonstrate that CLMs provide a simple and powerful model for capturing these differences, identifying named entity tokens in a diverse set of languages at close to the performance of full NER systems. Moreover, by adding very simple CLM-based features we can significantly improve the performance of an off-the-shelf NER system for multiple languages.</abstract>
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    <identifier type="doi">10.18653/v1/D18-1345</identifier>
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        <date>2018-oct-nov</date>
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%0 Conference Proceedings
%T On the Strength of Character Language Models for Multilingual Named Entity Recognition
%A Yu, Xiaodong
%A Mayhew, Stephen
%A Sammons, Mark
%A Roth, Dan
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F yu-etal-2018-strength
%X Character-level patterns have been widely used as features in English Named Entity Recognition (NER) systems. However, to date there has been no direct investigation of the inherent differences between name and nonname tokens in text, nor whether this property holds across multiple languages. This paper analyzes the capabilities of corpus-agnostic Character-level Language Models (CLMs) in the binary task of distinguishing name tokens from non-name tokens. We demonstrate that CLMs provide a simple and powerful model for capturing these differences, identifying named entity tokens in a diverse set of languages at close to the performance of full NER systems. Moreover, by adding very simple CLM-based features we can significantly improve the performance of an off-the-shelf NER system for multiple languages.
%R 10.18653/v1/D18-1345
%U https://aclanthology.org/D18-1345
%U https://doi.org/10.18653/v1/D18-1345
%P 3073-3077

Markdown (Informal)

[On the Strength of Character Language Models for Multilingual Named Entity Recognition](https://aclanthology.org/D18-1345) (Yu et al., EMNLP 2018)

• On the Strength of Character Language Models for Multilingual Named Entity Recognition (Yu et al., EMNLP 2018)

ACL

• Xiaodong Yu, Stephen Mayhew, Mark Sammons, and Dan Roth. 2018. On the Strength of Character Language Models for Multilingual Named Entity Recognition. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3073–3077, Brussels, Belgium. Association for Computational Linguistics.