Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/142638
Title: A Generative Model for category text generation
Authors: Li, Yang
Pan, Quan
Wang, Suhang
Yang, Tao
Cambria, Erik
Keywords: Engineering::Computer science and engineering
Issue Date: 2018
Source: Li, Y., Pan, Q., Wang, S., Yang, T., & Cambria, E. (2018). A Generative Model for category text generation. Information Sciences, 450, 301-315. doi:10.1016/j.ins.2018.03.050
Journal: Information Sciences
Abstract: The neural network model has been the fulcrum of the so-called AI revolution. Although very powerful for pattern-recognition tasks, however, the model has two main drawbacks: it tends to overfit when the training dataset is small, and it is unable to accurately capture category information when the class number is large. In this paper, we combine reinforcement learning, generative adversarial networks, and recurrent neural networks to build a new model, termed category sentence generative adversarial network (CS-GAN). Not only the proposed model is able to generate category sentences that enlarge the original dataset, but also it helps improve its generalization capability during supervised training. We evaluate the performance of CS-GAN for the task of sentiment analysis. Quantitative evaluation exhibits the accuracy improvement in polarity detection on a small dataset with high category information.
URI: https://hdl.handle.net/10356/142638
ISSN: 0020-0255
DOI: 10.1016/j.ins.2018.03.050
Rights: © 2018 Elsevier Inc. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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