Abstract
In this paper we describe a deep learning system that has been designed and built for the WASSA 2017 Emotion Intensity Shared Task. We introduce a representation learning approach based on inner attention on top of an RNN. Results show that our model offers good capabilities and is able to successfully identify emotion-bearing words to predict intensity without leveraging on lexicons, obtaining the 13th place among 22 shared task competitors.
Cite
CITATION STYLE
Marrese-Taylor, E., & Matsuo, Y. (2017). EmoAtt at EmoInt-2017: Inner attention sentence embedding for emotion intensity. In EMNLP 2017 - 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, WASSA 2017 - Proceedings of the Workshop (pp. 233–237). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/w17-5232
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