Survey on posterior regularization

Han Yanan
Liu Jianwei
Luo Xionglin
Dept. of Automation, China University of Petroleum, Beijing 102249, China

Abstract

During the model training, training corpora usually contains a lot of external problem-specific information that cannot be used by the model directly. Due to the flexibility and simplicity of its framework, Posterior regularization has been widely applied in the fields of natural language processing and sample selection of classification tasks. The aim of the posteriori regularization framework is to limit the capacity of the model posteriori on unmarked data, thus guiding the model to learn and achieve the desired performance. Firstly, this paper systematically described the problem of PR. Then it introduced the three general frameworks of PR, pointed the motivation of every PR and the specific form of every PR model, and the advantages of every PR model, the problems that every PR model could solve and the typical application scenarios that PR could be used. Lastly, this paper pointed out the research directions of the posterior regularization models in the future and summarized the content of the total paper.

Foundation Support

中国石油大学(北京)科研基金资助项目(2462020YXZZ023)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2020.12.0568
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 10
Section: Survey
Pages: 2881-2887,2903
Serial Number: 1001-3695(2021)10-001-2881-07

Publish History

[2021-10-05] Printed Article

Cite This Article

韩亚楠, 刘建伟, 罗雄麟. 后验正则化综述 [J]. 计算机应用研究, 2021, 38 (10): 2881-2887,2903. (Han Yanan, Liu Jianwei, Luo Xionglin. Survey on posterior regularization [J]. Application Research of Computers, 2021, 38 (10): 2881-2887,2903. )

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  • Application Research of Computers Monthly Journal
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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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