PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
An unsupervised approach to recognizing discourse relations
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Automatic detection of causal relations for Question Answering
MultiSumQA '03 Proceedings of the ACL 2003 workshop on Multilingual summarization and question answering - Volume 12
Learning to identify emotions in text
Proceedings of the 2008 ACM symposium on Applied computing
Affect Analysis of Web Forums and Blogs Using Correlation Ensembles
IEEE Transactions on Knowledge and Data Engineering
Learning semantic links from a corpus of parallel temporal and causal relations
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
Emotion classification using massive examples extracted from the web
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Semi-supervised cause identification from aviation safety reports
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
Information Processing and Management: an International Journal
A text-driven rule-based system for emotion cause detection
CAAGET '10 Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text
EMOCause: an easy-adaptable approach to emotion cause contexts
WASSA '11 Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis
Joint learning on sentiment and emotion classification
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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This paper proposes a multi-label approach to detect emotion causes. The multi-label model not only detects multi-clause causes, but also captures the long-distance information to facilitate emotion cause detection. In addition, based on the linguistic analysis, we create two sets of linguistic patterns during feature extraction. Both manually generalized patterns and automatically generalized patterns are designed to extract general cause expressions or specific constructions for emotion causes. Experiments show that our system achieves a performance much higher than a baseline model.