Report Transfer Learning of Deep Convolutional Network on Twitter

Hoa Le 1 Christophe Cerisara 1 Alexandre Denis 2
1 SYNALP - Natural Language Processing : representations, inference and semantics
LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : This report aims at showing the capacity of transfering a deep neural network on char-level on massive dataset Twitter, using distant supervision. We showed that more data could help for the Stanford140 dataset. The best overal result observed is 84% of transfer learning for two sentiment polarity classes (positive-negative) from 16M emoticons subjective SESAMm dataset to small SemEval 2013 dataset. Other learning of three classes (with neutral) or nine classes based on emoticons (happy, laughing, kisswink, playful, sad, horror, shock, annoyed, hesitated) didn't show any advantages yet in the study.
Type de document :
[Research Report] Loria & Inria Grand Est. 2017
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Contributeur : Thien Hoa Le <>
Soumis le : jeudi 13 juillet 2017 - 16:40:03
Dernière modification le : mardi 18 décembre 2018 - 16:38:02
Document(s) archivé(s) le : vendredi 26 janvier 2018 - 18:40:27


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  • HAL Id : hal-01562179, version 1


Hoa Le, Christophe Cerisara, Alexandre Denis. Report Transfer Learning of Deep Convolutional Network on Twitter. [Research Report] Loria & Inria Grand Est. 2017. 〈hal-01562179〉



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