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        <title>Cross-Domain Sentiment Classification with Target Domain Specific Information</title>
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        <namePart type="given">Minlong</namePart>
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        <namePart type="given">Yu-gang</namePart>
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    <name type="personal">
        <namePart type="given">Xuanjing</namePart>
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    <abstract>The task of adopting a model with good performance to a target domain that is different from the source domain used for training has received considerable attention in sentiment analysis. Most existing approaches mainly focus on learning representations that are domain-invariant in both the source and target domains. Few of them pay attention to domain-specific information, which should also be informative. In this work, we propose a method to simultaneously extract domain specific and invariant representations and train a classifier on each of the representation, respectively. And we introduce a few target domain labeled data for learning domain-specific information. To effectively utilize the target domain labeled data, we train the domain invariant representation based classifier with both the source and target domain labeled data and train the domain-specific representation based classifier with only the target domain labeled data. These two classifiers then boost each other in a co-training style. Extensive sentiment analysis experiments demonstrated that the proposed method could achieve better performance than state-of-the-art methods.</abstract>
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    <identifier type="doi">10.18653/v1/P18-1233</identifier>
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        <date>2018-07</date>
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%0 Conference Proceedings
%T Cross-Domain Sentiment Classification with Target Domain Specific Information
%A Peng, Minlong
%A Zhang, Qi
%A Jiang, Yu-gang
%A Huang, Xuanjing
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F peng-etal-2018-cross
%X The task of adopting a model with good performance to a target domain that is different from the source domain used for training has received considerable attention in sentiment analysis. Most existing approaches mainly focus on learning representations that are domain-invariant in both the source and target domains. Few of them pay attention to domain-specific information, which should also be informative. In this work, we propose a method to simultaneously extract domain specific and invariant representations and train a classifier on each of the representation, respectively. And we introduce a few target domain labeled data for learning domain-specific information. To effectively utilize the target domain labeled data, we train the domain invariant representation based classifier with both the source and target domain labeled data and train the domain-specific representation based classifier with only the target domain labeled data. These two classifiers then boost each other in a co-training style. Extensive sentiment analysis experiments demonstrated that the proposed method could achieve better performance than state-of-the-art methods.
%R 10.18653/v1/P18-1233
%U https://aclanthology.org/P18-1233
%U https://doi.org/10.18653/v1/P18-1233
%P 2505-2513

Markdown (Informal)

[Cross-Domain Sentiment Classification with Target Domain Specific Information](https://aclanthology.org/P18-1233) (Peng et al., ACL 2018)

• Cross-Domain Sentiment Classification with Target Domain Specific Information (Peng et al., ACL 2018)

ACL

• Minlong Peng, Qi Zhang, Yu-gang Jiang, and Xuanjing Huang. 2018. Cross-Domain Sentiment Classification with Target Domain Specific Information. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2505–2513, Melbourne, Australia. Association for Computational Linguistics.