Proisl T, Heinrich P, Kabashi B, Evert S (2018)
Publication Language: English
Publication Type: Conference contribution, Original article
Publication year: 2018
Publisher: Association for Computational Linguistics
City/Town: Brussels
Pages Range: 235–242
Conference Proceedings Title: Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
URI: http://aclweb.org/anthology/W18-6234
DOI: 10.18653/v1/w18-6234
Open Access Link: http://aclweb.org/anthology/W18-6234
EmotiKLUE is a submission to the Implicit Emotion Shared Task. It is a deep learning system that combines independent representations of the left and right contexts of the emotion word with the topic distribution of an LDA topic model. EmotiKLUE achieves a macro average F 1 score of 67.13%, significantly outperforming the baseline produced by a simple ML classifier. Further enhancements after the evaluation period lead to an improved F 1 score of 68.10%
APA:
Proisl, T., Heinrich, P., Kabashi, B., & Evert, S. (2018). EmotiKLUE at IEST 2018: Topic-Informed Classification of Implicit Emotions. In Balahur A, Mohammad SM, Hoste V, Klinger R (Eds.), Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (pp. 235–242). Brüssel, BE: Brussels: Association for Computational Linguistics.
MLA:
Proisl, Thomas, et al. "EmotiKLUE at IEST 2018: Topic-Informed Classification of Implicit Emotions." Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, Brüssel Ed. Balahur A, Mohammad SM, Hoste V, Klinger R, Brussels: Association for Computational Linguistics, 2018. 235–242.
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