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        <title>Adaptive Semi-supervised Learning for Cross-domain Sentiment Classification</title>
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        <namePart type="given">Ruidan</namePart>
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        <namePart type="given">Sun</namePart>
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    <name type="personal">
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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>
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            <namePart type="given">David</namePart>
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            <namePart type="given">Jun’ichi</namePart>
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    <abstract>We consider the cross-domain sentiment classification problem, where a sentiment classifier is to be learned from a source domain and to be generalized to a target domain. Our approach explicitly minimizes the distance between the source and the target instances in an embedded feature space. With the difference between source and target minimized, we then exploit additional information from the target domain by consolidating the idea of semi-supervised learning, for which, we jointly employ two regularizations — entropy minimization and self-ensemble bootstrapping — to incorporate the unlabeled target data for classifier refinement. Our experimental results demonstrate that the proposed approach can better leverage unlabeled data from the target domain and achieve substantial improvements over baseline methods in various experimental settings.</abstract>
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    <identifier type="doi">10.18653/v1/D18-1383</identifier>
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        <date>2018-oct-nov</date>
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%0 Conference Proceedings
%T Adaptive Semi-supervised Learning for Cross-domain Sentiment Classification
%A He, Ruidan
%A Lee, Wee Sun
%A Ng, Hwee Tou
%A Dahlmeier, Daniel
%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 he-etal-2018-adaptive
%X We consider the cross-domain sentiment classification problem, where a sentiment classifier is to be learned from a source domain and to be generalized to a target domain. Our approach explicitly minimizes the distance between the source and the target instances in an embedded feature space. With the difference between source and target minimized, we then exploit additional information from the target domain by consolidating the idea of semi-supervised learning, for which, we jointly employ two regularizations — entropy minimization and self-ensemble bootstrapping — to incorporate the unlabeled target data for classifier refinement. Our experimental results demonstrate that the proposed approach can better leverage unlabeled data from the target domain and achieve substantial improvements over baseline methods in various experimental settings.
%R 10.18653/v1/D18-1383
%U https://aclanthology.org/D18-1383
%U https://doi.org/10.18653/v1/D18-1383
%P 3467-3476

Markdown (Informal)

[Adaptive Semi-supervised Learning for Cross-domain Sentiment Classification](https://aclanthology.org/D18-1383) (He et al., EMNLP 2018)

• Adaptive Semi-supervised Learning for Cross-domain Sentiment Classification (He et al., EMNLP 2018)

ACL

• Ruidan He, Wee Sun Lee, Hwee Tou Ng, and Daniel Dahlmeier. 2018. Adaptive Semi-supervised Learning for Cross-domain Sentiment Classification. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3467–3476, Brussels, Belgium. Association for Computational Linguistics.