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• W2807476308 abstract "We propose a novel attentive hybrid GRU-based network (SAHGN), which we used at SemEval-2018 Task 1: Affect in Tweets. Our network has two main characteristics, 1) has the ability to internally optimize its feature representation using attention mechanisms, and 2) provides a hybrid representation using a character level Convolutional Neural Network (CNN), as well as a self-attentive word-level encoder. The key advantage of our model is its ability to signify the relevant and important information that enables self-optimization. Results are reported on the valence intensity regression task." @default.
• W2807476308 created "2018-06-13" @default.
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• W2807476308 date "2018-01-01" @default.
• W2807476308 modified "2023-09-23" @default.
• W2807476308 title "uOttawa at SemEval-2018 Task 1: Self-Attentive Hybrid GRU-Based Network" @default.
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• W2807476308 doi "https://doi.org/10.18653/v1/s18-1027" @default.
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