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.
|Title of host publication||Proceedings of the 14th International Conference on Empirical Methods on Natural Language Processing|
|Publisher||Association for Computational Linguistics|
|Number of pages||10|
|Publication status||Published - 11 Sep 2017|
|Event||2017 Conference on Empirical Methods in Natural Language Processing - |
Duration: 15 Sep 2017 → …
|Conference||2017 Conference on Empirical Methods in Natural Language Processing|
|Period||15/09/17 → …|
Bibliographical noteCopyright: Association of Computational Linguistics
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.