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        <title>Exploiting Document Knowledge for Aspect-level Sentiment Classification</title>
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            <title>Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)</title>
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            <namePart type="family">Gurevych</namePart>
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            <namePart type="given">Yusuke</namePart>
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    <abstract>Attention-based long short-term memory (LSTM) networks have proven to be useful in aspect-level sentiment classification. However, due to the difficulties in annotating aspect-level data, existing public datasets for this task are all relatively small, which largely limits the effectiveness of those neural models. In this paper, we explore two approaches that transfer knowledge from document-level data, which is much less expensive to obtain, to improve the performance of aspect-level sentiment classification. We demonstrate the effectiveness of our approaches on 4 public datasets from SemEval 2014, 2015, and 2016, and we show that attention-based LSTM benefits from document-level knowledge in multiple ways.</abstract>
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    <identifier type="doi">10.18653/v1/P18-2092</identifier>
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%0 Conference Proceedings
%T Exploiting Document Knowledge for Aspect-level Sentiment Classification
%A He, Ruidan
%A Lee, Wee Sun
%A Ng, Hwee Tou
%A Dahlmeier, Daniel
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F he-etal-2018-exploiting
%X Attention-based long short-term memory (LSTM) networks have proven to be useful in aspect-level sentiment classification. However, due to the difficulties in annotating aspect-level data, existing public datasets for this task are all relatively small, which largely limits the effectiveness of those neural models. In this paper, we explore two approaches that transfer knowledge from document-level data, which is much less expensive to obtain, to improve the performance of aspect-level sentiment classification. We demonstrate the effectiveness of our approaches on 4 public datasets from SemEval 2014, 2015, and 2016, and we show that attention-based LSTM benefits from document-level knowledge in multiple ways.
%R 10.18653/v1/P18-2092
%U https://aclanthology.org/P18-2092
%U https://doi.org/10.18653/v1/P18-2092
%P 579-585

Markdown (Informal)

[Exploiting Document Knowledge for Aspect-level Sentiment Classification](https://aclanthology.org/P18-2092) (He et al., ACL 2018)

• Exploiting Document Knowledge for Aspect-level Sentiment Classification (He et al., ACL 2018)

ACL

• Ruidan He, Wee Sun Lee, Hwee Tou Ng, and Daniel Dahlmeier. 2018. Exploiting Document Knowledge for Aspect-level Sentiment Classification. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 579–585, Melbourne, Australia. Association for Computational Linguistics.