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            <namePart type="given">Yusuke</namePart>
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    <abstract>One key task of fine-grained sentiment analysis of product reviews is to extract product aspects or features that users have expressed opinions on. This paper focuses on supervised aspect extraction using deep learning. Unlike other highly sophisticated supervised deep learning models, this paper proposes a novel and yet simple CNN model employing two types of pre-trained embeddings for aspect extraction: general-purpose embeddings and domain-specific embeddings. Without using any additional supervision, this model achieves surprisingly good results, outperforming state-of-the-art sophisticated existing methods. To our knowledge, this paper is the first to report such double embeddings based CNN model for aspect extraction and achieve very good results.</abstract>
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%0 Conference Proceedings
%T Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction
%A Xu, Hu
%A Liu, Bing
%A Shu, Lei
%A Yu, Philip S.
%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 xu-etal-2018-double
%X One key task of fine-grained sentiment analysis of product reviews is to extract product aspects or features that users have expressed opinions on. This paper focuses on supervised aspect extraction using deep learning. Unlike other highly sophisticated supervised deep learning models, this paper proposes a novel and yet simple CNN model employing two types of pre-trained embeddings for aspect extraction: general-purpose embeddings and domain-specific embeddings. Without using any additional supervision, this model achieves surprisingly good results, outperforming state-of-the-art sophisticated existing methods. To our knowledge, this paper is the first to report such double embeddings based CNN model for aspect extraction and achieve very good results.
%R 10.18653/v1/P18-2094
%U https://aclanthology.org/P18-2094
%U https://doi.org/10.18653/v1/P18-2094
%P 592-598

Markdown (Informal)

[Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction](https://aclanthology.org/P18-2094) (Xu et al., ACL 2018)

• Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction (Xu et al., ACL 2018)

ACL

• Hu Xu, Bing Liu, Lei Shu, and Philip S. Yu. 2018. Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 592–598, Melbourne, Australia. Association for Computational Linguistics.