@inproceedings{fan-etal-2019-knowledge,
title = "A Knowledge Regularized Hierarchical Approach for Emotion Cause Analysis",
author = "Fan, Chuang and
Yan, Hongyu and
Du, Jiachen and
Gui, Lin and
Bing, Lidong and
Yang, Min and
Xu, Ruifeng and
Mao, Ruibin",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1563",
doi = "10.18653/v1/D19-1563",
pages = "5614--5624",
abstract = "Emotion cause analysis, which aims to identify the reasons behind emotions, is a key topic in sentiment analysis. A variety of neural network models have been proposed recently, however, these previous models mostly focus on the learning architecture with local textual information, ignoring the discourse and prior knowledge, which play crucial roles in human text comprehension. In this paper, we propose a new method to extract emotion cause with a hierarchical neural model and knowledge-based regularizations, which aims to incorporate discourse context information and restrain the parameters by sentiment lexicon and common knowledge. The experimental results demonstrate that our proposed method achieves the state-of-the-art performance on two public datasets in different languages (Chinese and English), outperforming a number of competitive baselines by at least 2.08{\%} in F-measure.",
}
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<abstract>Emotion cause analysis, which aims to identify the reasons behind emotions, is a key topic in sentiment analysis. A variety of neural network models have been proposed recently, however, these previous models mostly focus on the learning architecture with local textual information, ignoring the discourse and prior knowledge, which play crucial roles in human text comprehension. In this paper, we propose a new method to extract emotion cause with a hierarchical neural model and knowledge-based regularizations, which aims to incorporate discourse context information and restrain the parameters by sentiment lexicon and common knowledge. The experimental results demonstrate that our proposed method achieves the state-of-the-art performance on two public datasets in different languages (Chinese and English), outperforming a number of competitive baselines by at least 2.08% in F-measure.</abstract>
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%0 Conference Proceedings
%T A Knowledge Regularized Hierarchical Approach for Emotion Cause Analysis
%A Fan, Chuang
%A Yan, Hongyu
%A Du, Jiachen
%A Gui, Lin
%A Bing, Lidong
%A Yang, Min
%A Xu, Ruifeng
%A Mao, Ruibin
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F fan-etal-2019-knowledge
%X Emotion cause analysis, which aims to identify the reasons behind emotions, is a key topic in sentiment analysis. A variety of neural network models have been proposed recently, however, these previous models mostly focus on the learning architecture with local textual information, ignoring the discourse and prior knowledge, which play crucial roles in human text comprehension. In this paper, we propose a new method to extract emotion cause with a hierarchical neural model and knowledge-based regularizations, which aims to incorporate discourse context information and restrain the parameters by sentiment lexicon and common knowledge. The experimental results demonstrate that our proposed method achieves the state-of-the-art performance on two public datasets in different languages (Chinese and English), outperforming a number of competitive baselines by at least 2.08% in F-measure.
%R 10.18653/v1/D19-1563
%U https://aclanthology.org/D19-1563
%U https://doi.org/10.18653/v1/D19-1563
%P 5614-5624
Markdown (Informal)
[A Knowledge Regularized Hierarchical Approach for Emotion Cause Analysis](https://aclanthology.org/D19-1563) (Fan et al., EMNLP-IJCNLP 2019)
ACL
- Chuang Fan, Hongyu Yan, Jiachen Du, Lin Gui, Lidong Bing, Min Yang, Ruifeng Xu, and Ruibin Mao. 2019. A Knowledge Regularized Hierarchical Approach for Emotion Cause Analysis. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 5614–5624, Hong Kong, China. Association for Computational Linguistics.