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Literary and Linguistic Computing 2002 17(4):401-412; doi:10.1093/llc/17.4.401
© 2002 by Association for Literary & Linguistic Computing
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Automatically Categorizing Written Texts by Author Gender

Moshe Koppel1, Shlomo Argamon1,2 and Anat Rachel Shimoni1

1 Department of Mathematics and Computer Science, Bar-Ilan University, Israel 2 Department of Computer Science, Jerusalem College of Technology, Israel

The problem of automatically determining the gender of a document's author would appear to be a more subtle problem than those of categorization by topic or authorship attribution. Nevertheless, it is shown that automated text categorization techniques can exploit combinations of simple lexical and syntactic features to infer the gender of the author of an unseen formal written document with approximately 80 per cent accuracy. The same techniques can be used to determine if a document is fiction or non-fiction with approximately 98 per cent accuracy.


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