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% accuracy. The same techniques can be used to determine if a document is fiction or non-fiction with approximately 98% accuracy.
In REF , combinations of simple lexical and syntactic features were used to infer the author gender of the British National Corpus (BNC) with 80% accuracy.
Automatically categorizing written texts by author gender
Literary and Linguistic Computing
Computer Science