@inproceedings{shen-etal-2018-sentiment,
title = "Sentiment Classification towards Question-Answering with Hierarchical Matching Network",
author = "Shen, Chenlin and
Sun, Changlong and
Wang, Jingjing and
Kang, Yangyang and
Li, Shoushan and
Liu, Xiaozhong and
Si, Luo and
Zhang, Min and
Zhou, Guodong",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1401",
doi = "10.18653/v1/D18-1401",
pages = "3654--3663",
abstract = "In an e-commerce environment, user-oriented question-answering (QA) text pair could carry rich sentiment information. In this study, we propose a novel task/method to address QA sentiment analysis. In particular, we create a high-quality annotated corpus with specially-designed annotation guidelines for QA-style sentiment classification. On the basis, we propose a three-stage hierarchical matching network to explore deep sentiment information in a QA text pair. First, we segment both the question and answer text into sentences and construct a number of [Q-sentence, A-sentence] units in each QA text pair. Then, by leveraging a QA bidirectional matching layer, the proposed approach can learn the matching vectors of each [Q-sentence, A-sentence] unit. Finally, we characterize the importance of the generated matching vectors via a self-matching attention layer. Experimental results, comparing with a number of state-of-the-art baselines, demonstrate the impressive effectiveness of the proposed approach for QA-style sentiment classification.",
}
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<abstract>In an e-commerce environment, user-oriented question-answering (QA) text pair could carry rich sentiment information. In this study, we propose a novel task/method to address QA sentiment analysis. In particular, we create a high-quality annotated corpus with specially-designed annotation guidelines for QA-style sentiment classification. On the basis, we propose a three-stage hierarchical matching network to explore deep sentiment information in a QA text pair. First, we segment both the question and answer text into sentences and construct a number of [Q-sentence, A-sentence] units in each QA text pair. Then, by leveraging a QA bidirectional matching layer, the proposed approach can learn the matching vectors of each [Q-sentence, A-sentence] unit. Finally, we characterize the importance of the generated matching vectors via a self-matching attention layer. Experimental results, comparing with a number of state-of-the-art baselines, demonstrate the impressive effectiveness of the proposed approach for QA-style sentiment classification.</abstract>
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%0 Conference Proceedings
%T Sentiment Classification towards Question-Answering with Hierarchical Matching Network
%A Shen, Chenlin
%A Sun, Changlong
%A Wang, Jingjing
%A Kang, Yangyang
%A Li, Shoushan
%A Liu, Xiaozhong
%A Si, Luo
%A Zhang, Min
%A Zhou, Guodong
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F shen-etal-2018-sentiment
%X In an e-commerce environment, user-oriented question-answering (QA) text pair could carry rich sentiment information. In this study, we propose a novel task/method to address QA sentiment analysis. In particular, we create a high-quality annotated corpus with specially-designed annotation guidelines for QA-style sentiment classification. On the basis, we propose a three-stage hierarchical matching network to explore deep sentiment information in a QA text pair. First, we segment both the question and answer text into sentences and construct a number of [Q-sentence, A-sentence] units in each QA text pair. Then, by leveraging a QA bidirectional matching layer, the proposed approach can learn the matching vectors of each [Q-sentence, A-sentence] unit. Finally, we characterize the importance of the generated matching vectors via a self-matching attention layer. Experimental results, comparing with a number of state-of-the-art baselines, demonstrate the impressive effectiveness of the proposed approach for QA-style sentiment classification.
%R 10.18653/v1/D18-1401
%U https://aclanthology.org/D18-1401
%U https://doi.org/10.18653/v1/D18-1401
%P 3654-3663
Markdown (Informal)
[Sentiment Classification towards Question-Answering with Hierarchical Matching Network](https://aclanthology.org/D18-1401) (Shen et al., EMNLP 2018)
ACL
- Chenlin Shen, Changlong Sun, Jingjing Wang, Yangyang Kang, Shoushan Li, Xiaozhong Liu, Luo Si, Min Zhang, and Guodong Zhou. 2018. Sentiment Classification towards Question-Answering with Hierarchical Matching Network. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3654–3663, Brussels, Belgium. Association for Computational Linguistics.