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A Hybrid BERT and LightGBM based Model for Predicting Emotion GIF Categories on Twitter
August 14, 2020 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
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Authors
Ye Bi, Shuo Wang, Zhongrui Fan
arXiv ID
2008.06176
Category
cs.IR: Information Retrieval
Cross-listed
cs.CL
Citations
1
Venue
arXiv.org
Last Checked
4 months ago
Abstract
The animated Graphical Interchange Format (GIF) images have been widely used on social media as an intuitive way of expression emotion. Given their expressiveness, GIFs offer a more nuanced and precise way to convey emotions. In this paper, we present our solution for the EmotionGIF 2020 challenge, the shared task of SocialNLP 2020. To recommend GIF categories for unlabeled tweets, we regarded this problem as a kind of matching tasks and proposed a learning to rank framework based on Bidirectional Encoder Representations from Transformer (BERT) and LightGBM. Our team won the 4th place with a Mean Average Precision @ 6 (MAP@6) score of 0.5394 on the round 1 leaderboard.
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