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	Tri-Training for authorship attribution with limited training data: a comprehensive study
	Qian, TY ; Liu, B ; Chen, L ; Peng, ZY ; Zhong, M ; He, GL ; Li, XH ; Xu, G
刊名	NEUROCOMPUTING
	2016
卷号	171页码:798-806
关键词	Authorship attribution Tri-Training Limited training data
ISSN号	0925-2312
中文摘要	Authorship attribution (AA) aims to identify the authors of a set of documents. Traditional studies in this area often assume that there are a large set of labeled documents available for training. However, in the real life, it is often difficult or expensive to collect a large set of labeled data. For example, in the online review domain, most reviewers (authors) only write a few reviews, which are not enough to serve as the training data for accurate classification. In this paper, we present a novel three-view Tri-Training method to iteratively identify authors of unlabeled data to augment the training set. The key idea is to first represent each document in three distinct views, and then perform Tri-Training to exploit the large amount of unlabeled documents. Starting from 10 training documents per author, we systematically evaluate the effectiveness of the proposed Tri-Training method for AA. Experimental results show that the proposed approach outperforms the state-of-the-art semi-supervised method CNG-FSVM and other baselines. (C) 2015 Elsevier B.V. All rights reserved.
英文摘要	Authorship attribution (AA) aims to identify the authors of a set of documents. Traditional studies in this area often assume that there are a large set of labeled documents available for training. However, in the real life, it is often difficult or expensive to collect a large set of labeled data. For example, in the online review domain, most reviewers (authors) only write a few reviews, which are not enough to serve as the training data for accurate classification. In this paper, we present a novel three-view Tri-Training method to iteratively identify authors of unlabeled data to augment the training set. The key idea is to first represent each document in three distinct views, and then perform Tri-Training to exploit the large amount of unlabeled documents. Starting from 10 training documents per author, we systematically evaluate the effectiveness of the proposed Tri-Training method for AA. Experimental results show that the proposed approach outperforms the state-of-the-art semi-supervised method CNG-FSVM and other baselines. (C) 2015 Elsevier B.V. All rights reserved.
收录类别	SCI
语种	英语
WOS记录号	WOS:000364883900080
公开日期	2016-12-13
内容类型	期刊论文
源URL	[http://ir.iscas.ac.cn/handle/311060/17416]
专题	软件研究所_软件所图书馆_期刊论文
推荐引用方式 GB/T 7714	Qian, TY,Liu, B,Chen, L,et al. Tri-Training for authorship attribution with limited training data: a comprehensive study[J]. NEUROCOMPUTING,2016,171:798-806.
APA	Qian, TY.,Liu, B.,Chen, L.,Peng, ZY.,Zhong, M.,...&Xu, G.(2016).Tri-Training for authorship attribution with limited training data: a comprehensive study.NEUROCOMPUTING,171,798-806.
MLA	Qian, TY,et al."Tri-Training for authorship attribution with limited training data: a comprehensive study".NEUROCOMPUTING 171(2016):798-806.

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