A question answering approach for emotion cause extraction


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.

Divisions: College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
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Additional Information: Copyright: Association of Computational Linguistics
Event Title: 2017 Conference on Empirical Methods in Natural Language Processing
Event Type: Other
Event Dates: 2017-09-15
ISBN: 978-1-945626-97-5
Last Modified: 18 Mar 2024 08:08
Date Deposited: 17 Oct 2017 07:18
PURE Output Type: Conference contribution
Published Date: 2017-09-11
Published Online Date: 2017-09-11
Accepted Date: 2017-09-11
Authors: Gui, Lin
Hu, Jiannan
He, Yulan (ORCID Profile 0000-0003-3948-5845)
Xu, Ruifeng
Lu, Qin
Du, Jiachen



Version: Accepted Version

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