Li, Tao and Liu, Xudong and Su, Shihan (2018) Semi-supervised Text Regression with Conditional Generative Adversarial Networks. In: 2018 IEEE International Conference on Big Data (Big Data). IEEE , Piscataway, NJ, pp. 5375-5377. ISBN 9781538650356. https://resolver.caltech.edu/CaltechAUTHORS:20190131-131445365
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Abstract
Enormous online textual information provides intriguing opportunities for understandings of social and economic semantics. In this paper, we propose a novel text regression model based on a conditional generative adversarial network (GAN), with an attempt to associate textual data and social outcomes in a semi-supervised manner. Besides promising potential of predicting capabilities, our superiorities are twofold: (i) the model works with unbalanced datasets of limited labelled data, which align with real-world scenarios; and (ii) predictions are obtained by an end-to-end framework, without explicitly selecting high-level representations. Finally we point out related datasets for experiments and future research directions.
Item Type: | Book Section | |||||||||
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Additional Information: | © 2018 IEEE. We thank Hao Peng and Kantapon Kaewtip for insightful discussions. The idea of this work originally came out during discussions of [29] and [30]. | |||||||||
DOI: | 10.1109/bigdata.2018.8622140 | |||||||||
Record Number: | CaltechAUTHORS:20190131-131445365 | |||||||||
Persistent URL: | https://resolver.caltech.edu/CaltechAUTHORS:20190131-131445365 | |||||||||
Official Citation: | T. Li, X. Liu and S. Su, "Semi-supervised Text Regression with Conditional Generative Adversarial Networks," 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA, 2018, pp. 5375-5377. doi: 10.1109/BigData.2018.8622140 | |||||||||
Usage Policy: | No commercial reproduction, distribution, display or performance rights in this work are provided. | |||||||||
ID Code: | 92549 | |||||||||
Collection: | CaltechAUTHORS | |||||||||
Deposited By: | George Porter | |||||||||
Deposited On: | 31 Jan 2019 23:28 | |||||||||
Last Modified: | 16 Nov 2021 03:51 |
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