@inproceedings{gui-etal-2017-question,
title = "A Question Answering Approach for Emotion Cause Extraction",
author = "Gui, Lin and
Hu, Jiannan and
He, Yulan and
Xu, Ruifeng and
Lu, Qin and
Du, Jiachen",
editor = "Palmer, Martha and
Hwa, Rebecca and
Riedel, Sebastian",
booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D17-1167",
doi = "10.18653/v1/D17-1167",
pages = "1593--1602",
abstract = "Emotion cause extraction aims to identify the reasons behind a certain emotion expressed in text. It is a much more difficult task compared to emotion classification. Inspired by recent advances in using deep memory networks for question answering (QA), we propose a new approach which considers emotion cause identification as a reading comprehension task in QA. Inspired by convolutional neural networks, we propose a new mechanism to store relevant context in different memory slots to model context information. Our proposed approach can extract both word level sequence features and lexical features. Performance evaluation shows that our method achieves the state-of-the-art performance on a recently released emotion cause dataset, outperforming a number of competitive baselines by at least 3.01{\%} in F-measure.",
}
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<abstract>Emotion cause extraction aims to identify the reasons behind a certain emotion expressed in text. It is a much more difficult task compared to emotion classification. Inspired by recent advances in using deep memory networks for question answering (QA), we propose a new approach which considers emotion cause identification as a reading comprehension task in QA. Inspired by convolutional neural networks, we propose a new mechanism to store relevant context in different memory slots to model context information. Our proposed approach can extract both word level sequence features and lexical features. Performance evaluation shows that our method achieves the state-of-the-art performance on a recently released emotion cause dataset, outperforming a number of competitive baselines by at least 3.01% in F-measure.</abstract>
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%0 Conference Proceedings
%T A Question Answering Approach for Emotion Cause Extraction
%A Gui, Lin
%A Hu, Jiannan
%A He, Yulan
%A Xu, Ruifeng
%A Lu, Qin
%A Du, Jiachen
%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 gui-etal-2017-question
%X Emotion cause extraction aims to identify the reasons behind a certain emotion expressed in text. It is a much more difficult task compared to emotion classification. Inspired by recent advances in using deep memory networks for question answering (QA), we propose a new approach which considers emotion cause identification as a reading comprehension task in QA. Inspired by convolutional neural networks, we propose a new mechanism to store relevant context in different memory slots to model context information. Our proposed approach can extract both word level sequence features and lexical features. Performance evaluation shows that our method achieves the state-of-the-art performance on a recently released emotion cause dataset, outperforming a number of competitive baselines by at least 3.01% in F-measure.
%R 10.18653/v1/D17-1167
%U https://aclanthology.org/D17-1167
%U https://doi.org/10.18653/v1/D17-1167
%P 1593-1602
Markdown (Informal)
[A Question Answering Approach for Emotion Cause Extraction](https://aclanthology.org/D17-1167) (Gui et al., EMNLP 2017)
ACL
- Lin Gui, Jiannan Hu, Yulan He, Ruifeng Xu, Qin Lu, and Jiachen Du. 2017. A Question Answering Approach for Emotion Cause Extraction. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1593–1602, Copenhagen, Denmark. Association for Computational Linguistics.