在中文文章里可否根据用词习惯判断作者呢
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回复: 在中文文章里可否根据用词习惯判断作者呢
不仅仅是用词，在句法，语义方面都会存在个体差异。Here is a paper concerning authorship identification.
A framework for authorship identification of online messages: Writing-style features and classification techniques
Source Journal of the American Society for Information Science and Technology archive
Volume 57 , Issue 3 (February 2006) table of contents
Pages: 378 - 393
Year of Publication: 2006
ISSN:1532-2882
Authors Rong Zheng Information, Operations, and Management Science, Stern School of Business, New York University, New York, NY 10012
Jiexun Li Artificial Intelligence Lab, Department of Management Information Systems, University of Arizona, Tucson, AZ 85721
Hsinchun Chen Artificial Intelligence Lab, Department of Management Information Systems, University of Arizona, Tucson, AZ 85721
Zan Huang Artificial Intelligence Lab, Department of Management Information Systems, University of Arizona, Tucson, AZ 85721
With the rapid proliferation of Internet technologies and applications, misuse of online messages for inappropriate or illegal purposes has become a major concern for society. The anonymous nature of online-message distribution makes identity tracing a critical problem. We developed a framework for authorship identification of online messages to address the identity-tracing problem. In this framework, four types of writing-style features (lexical, syntactic, structural, and content-specific features) are extracted and inductive learning algorithms are used to build feature-based classification models to identify authorship of online messages. To examine this framework, we conducted experiments on English and Chinese online-newsgroup messages. We compared the discriminating power of the four types of features and of three classification techniques: decision trees, backpropagation neural networks, and support vector machines. The experimental results showed that the proposed approach was able to identify authors of online messages with satisfactory accuracy of 70 to 95%. All four types of message features contributed to discriminating authors of online messages. Support vector machines outperformed the other two classification techniques in our experiments. The high performance we achieved for both the English and Chinese datasets showed the potential of this approach in a multiple-language context. ? 2006 Wiley Periodicals, Inc.
不仅仅是用词，在句法，语义方面都会存在个体差异。Here is a paper concerning authorship identification.
A framework for authorship identification of online messages: Writing-style features and classification techniques
Source Journal of the American Society for Information Science and Technology archive
Volume 57 , Issue 3 (February 2006) table of contents
Pages: 378 - 393
Year of Publication: 2006
ISSN:1532-2882
Authors Rong Zheng Information, Operations, and Management Science, Stern School of Business, New York University, New York, NY 10012
Jiexun Li Artificial Intelligence Lab, Department of Management Information Systems, University of Arizona, Tucson, AZ 85721
Hsinchun Chen Artificial Intelligence Lab, Department of Management Information Systems, University of Arizona, Tucson, AZ 85721
Zan Huang Artificial Intelligence Lab, Department of Management Information Systems, University of Arizona, Tucson, AZ 85721
With the rapid proliferation of Internet technologies and applications, misuse of online messages for inappropriate or illegal purposes has become a major concern for society. The anonymous nature of online-message distribution makes identity tracing a critical problem. We developed a framework for authorship identification of online messages to address the identity-tracing problem. In this framework, four types of writing-style features (lexical, syntactic, structural, and content-specific features) are extracted and inductive learning algorithms are used to build feature-based classification models to identify authorship of online messages. To examine this framework, we conducted experiments on English and Chinese online-newsgroup messages. We compared the discriminating power of the four types of features and of three classification techniques: decision trees, backpropagation neural networks, and support vector machines. The experimental results showed that the proposed approach was able to identify authors of online messages with satisfactory accuracy of 70 to 95%. All four types of message features contributed to discriminating authors of online messages. Support vector machines outperformed the other two classification techniques in our experiments. The high performance we achieved for both the English and Chinese datasets showed the potential of this approach in a multiple-language context. ? 2006 Wiley Periodicals, Inc.
回复: 在中文文章里可否根据用词习惯判断作者呢
There is an old post here, where you can see a concrete example even though this is not exactly a corpus-based study.
http://forum.corpus4u.org/showthread.php?t=874
There is an old post here, where you can see a concrete example even though this is not exactly a corpus-based study.
http://forum.corpus4u.org/showthread.php?t=874
回复: 在中文文章里可否根据用词习惯判断作者呢
Your questions relate to the use of corpora in literary/stylistic studies and authorship atttribution in forensic linguistics. Please refer to Corpus-based Language Studies (London: Routledge, 2006) Units A10.13-A10.14 for a recent review of the work in these areas.
Your questions relate to the use of corpora in literary/stylistic studies and authorship atttribution in forensic linguistics. Please refer to Corpus-based Language Studies (London: Routledge, 2006) Units A10.13-A10.14 for a recent review of the work in these areas.