A question answering approach for emotion cause extraction

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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.



Publication date11 Sep 2017
Publication titleProceedings of the 14th International Conference on Empirical Methods on Natural Language Processing
PublisherAssociation for Computational Linguistics
Number of pages10
ISBN (Electronic)978-1-945626-97-5
Original languageEnglish
Event2017 Conference on Empirical Methods in Natural Language Processing -


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

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Copyright: Association of Computational Linguistics

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