A question answering approach for emotion cause extraction

Lin Gui, Jiannan Hu, Yulan He, Ruifeng Xu, Qin Lu, Jiachen Du

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.
Original languageEnglish
Title of host publicationProceedings of the 14th International Conference on Empirical Methods on Natural Language Processing
PublisherAssociation for Computational Linguistics
Pages1594-1603
Number of pages10
ISBN (Electronic)978-1-945626-97-5
Publication statusPublished - 11 Sep 2017
Event2017 Conference on Empirical Methods in Natural Language Processing -
Duration: 15 Sep 2017 → …

Conference

Conference2017 Conference on Empirical Methods in Natural Language Processing
Period15/09/17 → …

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Bibliographical note

Copyright: Association of Computational Linguistics

Cite this

Gui, L., Hu, J., He, Y., Xu, R., Lu, Q., & Du, J. (2017). A question answering approach for emotion cause extraction. In Proceedings of the 14th International Conference on Empirical Methods on Natural Language Processing (pp. 1594-1603). Association for Computational Linguistics.
Gui, Lin ; Hu, Jiannan ; He, Yulan ; Xu, Ruifeng ; Lu, Qin ; Du, Jiachen. / A question answering approach for emotion cause extraction. Proceedings of the 14th International Conference on Empirical Methods on Natural Language Processing. Association for Computational Linguistics, 2017. pp. 1594-1603
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title = "A question answering approach for emotion cause extraction",
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.",
author = "Lin Gui and Jiannan Hu and Yulan He and Ruifeng Xu and Qin Lu and Jiachen Du",
note = "Copyright: Association of Computational Linguistics",
year = "2017",
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Gui, L, Hu, J, He, Y, Xu, R, Lu, Q & Du, J 2017, A question answering approach for emotion cause extraction. in Proceedings of the 14th International Conference on Empirical Methods on Natural Language Processing. Association for Computational Linguistics, pp. 1594-1603, 2017 Conference on Empirical Methods in Natural Language Processing , 15/09/17.

A question answering approach for emotion cause extraction. / Gui, Lin; Hu, Jiannan; He, Yulan; Xu, Ruifeng; Lu, Qin; Du, Jiachen.

Proceedings of the 14th International Conference on Empirical Methods on Natural Language Processing. Association for Computational Linguistics, 2017. p. 1594-1603.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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T1 - A question answering approach for emotion cause extraction

AU - Gui, Lin

AU - Hu, Jiannan

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AU - Xu, Ruifeng

AU - Lu, Qin

AU - Du, Jiachen

N1 - Copyright: Association of Computational Linguistics

PY - 2017/9/11

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N2 - 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.

AB - 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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BT - Proceedings of the 14th International Conference on Empirical Methods on Natural Language Processing

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Gui L, Hu J, He Y, Xu R, Lu Q, Du J. A question answering approach for emotion cause extraction. In Proceedings of the 14th International Conference on Empirical Methods on Natural Language Processing. Association for Computational Linguistics. 2017. p. 1594-1603