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        <title>DOER: Dual Cross-Shared RNN for Aspect Term-Polarity Co-Extraction</title>
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        <namePart type="given">Huaishao</namePart>
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    <abstract>This paper focuses on two related subtasks of aspect-based sentiment analysis, namely aspect term extraction and aspect sentiment classification, which we call aspect term-polarity co-extraction. The former task is to extract aspects of a product or service from an opinion document, and the latter is to identify the polarity expressed in the document about these extracted aspects. Most existing algorithms address them as two separate tasks and solve them one by one, or only perform one task, which can be complicated for real applications. In this paper, we treat these two tasks as two sequence labeling problems and propose a novel Dual crOss-sharEd RNN framework (DOER) to generate all aspect term-polarity pairs of the input sentence simultaneously. Specifically, DOER involves a dual recurrent neural network to extract the respective representation of each task, and a cross-shared unit to consider the relationship between them. Experimental results demonstrate that the proposed framework outperforms state-of-the-art baselines on three benchmark datasets.</abstract>
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    <identifier type="doi">10.18653/v1/P19-1056</identifier>
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%0 Conference Proceedings
%T DOER: Dual Cross-Shared RNN for Aspect Term-Polarity Co-Extraction
%A Luo, Huaishao
%A Li, Tianrui
%A Liu, Bing
%A Zhang, Junbo
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F luo-etal-2019-doer
%X This paper focuses on two related subtasks of aspect-based sentiment analysis, namely aspect term extraction and aspect sentiment classification, which we call aspect term-polarity co-extraction. The former task is to extract aspects of a product or service from an opinion document, and the latter is to identify the polarity expressed in the document about these extracted aspects. Most existing algorithms address them as two separate tasks and solve them one by one, or only perform one task, which can be complicated for real applications. In this paper, we treat these two tasks as two sequence labeling problems and propose a novel Dual crOss-sharEd RNN framework (DOER) to generate all aspect term-polarity pairs of the input sentence simultaneously. Specifically, DOER involves a dual recurrent neural network to extract the respective representation of each task, and a cross-shared unit to consider the relationship between them. Experimental results demonstrate that the proposed framework outperforms state-of-the-art baselines on three benchmark datasets.
%R 10.18653/v1/P19-1056
%U https://aclanthology.org/P19-1056
%U https://doi.org/10.18653/v1/P19-1056
%P 591-601

Markdown (Informal)

[DOER: Dual Cross-Shared RNN for Aspect Term-Polarity Co-Extraction](https://aclanthology.org/P19-1056) (Luo et al., ACL 2019)

• DOER: Dual Cross-Shared RNN for Aspect Term-Polarity Co-Extraction (Luo et al., ACL 2019)

ACL

• Huaishao Luo, Tianrui Li, Bing Liu, and Junbo Zhang. 2019. DOER: Dual Cross-Shared RNN for Aspect Term-Polarity Co-Extraction. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 591–601, Florence, Italy. Association for Computational Linguistics.