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An empirical evaluation of SVM on meta features for authorship attribution of online texts

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成果类型:
期刊论文、会议论文
作者:
Yao, Hongwei;Qian, Tieyun;Chen, Li;Qian, Manyun;Mo, Xueyu
作者机构:
[Qian, Tieyun; Yao, Hongwei] State Key Laboratory of Software Engineering, Wuhan University, Wuhan, China
[Chen, Li; Qian, Manyun; Mo, Xueyu] Department of Computer Science, Central China Normal University, Wuhan, China
语种:
英文
关键词:
Authorship attribution;Authorship identification;Classification methods;Comparative evaluations;Feature identification;Machine learning approaches;Machine learning techniques;Meta-features;Experiments;Learning systems
期刊:
Lecture Notes in Computer Science
ISSN:
0302-9743
年:
2013
卷:
8284 LNAI
页码:
28-37
会议名称:
1st International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2013
会议时间:
18 December 2013 through 20 December 2013
会议地点:
Tamil Nadu
ISBN:
9783319038438
机构署名:
本校为其他机构
院系归属:
计算机学院
摘要:
Authorship attribution (AA) has been studied by many researchers. Recently, with the widespread of online texts, authorship attribution of online texts starts to receive a great deal of attentions. The essence of this problem is to identify a set of features that can capture the writing styles of an author. However, previous studies on feature identification mainly used statistical methods and conducted out experiments on small data sets, i.e., less than 10. This scale is distance from the real application of AA of online texts. In addition, due to the special characteristics of online texts, ...

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