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        <title>Document-Level Multi-Aspect Sentiment Classification as Machine Comprehension</title>
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        <namePart type="given">Yichun</namePart>
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            <title>Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Rebecca</namePart>
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    <abstract>Document-level multi-aspect sentiment classification is an important task for customer relation management. In this paper, we model the task as a machine comprehension problem where pseudo question-answer pairs are constructed by a small number of aspect-related keywords and aspect ratings. A hierarchical iterative attention model is introduced to build aspectspecific representations by frequent and repeated interactions between documents and aspect questions. We adopt a hierarchical architecture to represent both word level and sentence level information, and use the attention operations for aspect questions and documents alternatively with the multiple hop mechanism. Experimental results on the TripAdvisor and BeerAdvocate datasets show that our model outperforms classical baselines. We will release our code and data for the method replicability.</abstract>
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        <date>2017-09</date>
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            <start>2044</start>
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%0 Conference Proceedings
%T Document-Level Multi-Aspect Sentiment Classification as Machine Comprehension
%A Yin, Yichun
%A Song, Yangqiu
%A Zhang, Ming
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F yin-etal-2017-document
%X Document-level multi-aspect sentiment classification is an important task for customer relation management. In this paper, we model the task as a machine comprehension problem where pseudo question-answer pairs are constructed by a small number of aspect-related keywords and aspect ratings. A hierarchical iterative attention model is introduced to build aspectspecific representations by frequent and repeated interactions between documents and aspect questions. We adopt a hierarchical architecture to represent both word level and sentence level information, and use the attention operations for aspect questions and documents alternatively with the multiple hop mechanism. Experimental results on the TripAdvisor and BeerAdvocate datasets show that our model outperforms classical baselines. We will release our code and data for the method replicability.
%R 10.18653/v1/D17-1217
%U https://aclanthology.org/D17-1217/
%U https://doi.org/10.18653/v1/D17-1217
%P 2044-2054

Markdown (Informal)

[Document-Level Multi-Aspect Sentiment Classification as Machine Comprehension](https://aclanthology.org/D17-1217/) (Yin et al., EMNLP 2017)

• Document-Level Multi-Aspect Sentiment Classification as Machine Comprehension (Yin et al., EMNLP 2017)

ACL

• Yichun Yin, Yangqiu Song, and Ming Zhang. 2017. Document-Level Multi-Aspect Sentiment Classification as Machine Comprehension. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2044–2054, Copenhagen, Denmark. Association for Computational Linguistics.