CaltechAUTHORS
  A Caltech Library Service

Semi-supervised Text Regression with Conditional Generative Adversarial Networks

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. http://resolver.caltech.edu/CaltechAUTHORS:20190131-131445365

[img] PDF - Published Version
See Usage Policy.

183Kb
[img] PDF - Accepted Version
See Usage Policy.

151Kb

Use this Persistent URL to link to this item: http://resolver.caltech.edu/CaltechAUTHORS:20190131-131445365

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
Related URLs:
URLURL TypeDescription
https://doi.org/10.1109/bigdata.2018.8622140DOIArticle
https://arxiv.org/abs/1810.01165arXivDiscussion Paper
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].
Record Number:CaltechAUTHORS:20190131-131445365
Persistent URL:http://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:31 Jan 2019 23:28

Repository Staff Only: item control page