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    <titleInfo>
        <title>Neural Adaptation Layers for Cross-domain Named Entity Recognition</title>
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    <name type="personal">
        <namePart type="given">Bill</namePart>
        <namePart type="given">Yuchen</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="family">Hockenmaier</namePart>
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    <abstract>Recent research efforts have shown that neural architectures can be effective in conventional information extraction tasks such as named entity recognition, yielding state-of-the-art results on standard newswire datasets. However, despite significant resources required for training such models, the performance of a model trained on one domain typically degrades dramatically when applied to a different domain, yet extracting entities from new emerging domains such as social media can be of significant interest. In this paper, we empirically investigate effective methods for conveniently adapting an existing, well-trained neural NER model for a new domain. Unlike existing approaches, we propose lightweight yet effective methods for performing domain adaptation for neural models. Specifically, we introduce adaptation layers on top of existing neural architectures, where no re-training using the source domain data is required. We conduct extensive empirical studies and show that our approach significantly outperforms state-of-the-art methods.</abstract>
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    <identifier type="doi">10.18653/v1/D18-1226</identifier>
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        <date>2018-oct-nov</date>
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            <start>2012</start>
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%0 Conference Proceedings
%T Neural Adaptation Layers for Cross-domain Named Entity Recognition
%A Lin, Bill Yuchen
%A Lu, Wei
%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 lin-lu-2018-neural
%X Recent research efforts have shown that neural architectures can be effective in conventional information extraction tasks such as named entity recognition, yielding state-of-the-art results on standard newswire datasets. However, despite significant resources required for training such models, the performance of a model trained on one domain typically degrades dramatically when applied to a different domain, yet extracting entities from new emerging domains such as social media can be of significant interest. In this paper, we empirically investigate effective methods for conveniently adapting an existing, well-trained neural NER model for a new domain. Unlike existing approaches, we propose lightweight yet effective methods for performing domain adaptation for neural models. Specifically, we introduce adaptation layers on top of existing neural architectures, where no re-training using the source domain data is required. We conduct extensive empirical studies and show that our approach significantly outperforms state-of-the-art methods.
%R 10.18653/v1/D18-1226
%U https://aclanthology.org/D18-1226
%U https://doi.org/10.18653/v1/D18-1226
%P 2012-2022

Markdown (Informal)

[Neural Adaptation Layers for Cross-domain Named Entity Recognition](https://aclanthology.org/D18-1226) (Lin & Lu, EMNLP 2018)

• Neural Adaptation Layers for Cross-domain Named Entity Recognition (Lin & Lu, EMNLP 2018)

ACL

• Bill Yuchen Lin and Wei Lu. 2018. Neural Adaptation Layers for Cross-domain Named Entity Recognition. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2012–2022, Brussels, Belgium. Association for Computational Linguistics.