Automatically Categorizing Written Texts by Author Gender

by: Moshe Koppel, Shlomo Argamon, Anat R Shimoni

Lit Linguist Computing, Vol. 17, No. 4. (1 November 2002), pp. 401-412.

View FullText article

DOI, HighWire

Reviews [Write a review of this article]

There are no reviews of this article

Find related articles from these CiteULike users

isido, STS (group), feminist_technoscience (group), Academic Writing, Literacies and Discourse (group), kaniko, craigtalbert

Find related articles with these CiteULike tags

author, categorization, computational, gender, lexical, no-tag, syntax, technology, text

Abstract

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. 10.1093/llc/17.4.401

@article{citeulike:1019645, abstract = {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. 10.1093/llc/17.4.401}, author = {Koppel, Moshe and Argamon, Shlomo and Shimoni, Anat R. }, citeulike-article-id = {1019645}, doi = {10.1093/llc/17.4.401}, journal = {Lit Linguist Computing}, keywords = {author, categorization, computational, gender, lexical, syntax, technology, text}, month = {November}, number = {4}, pages = {401--412}, posted-at = {2008-07-17 09:11:27}, priority = {2}, title = {Automatically Categorizing Written Texts by Author Gender}, url = {http://dx.doi.org/10.1093/llc/17.4.401}, volume = {17}, year = {2002} }

RIS record

TY - JOUR ID - citeulike:1019645 L3 - citeulike-article-id:1019645 TI - Automatically Categorizing Written Texts by Author Gender JF - Lit Linguist Computing VL - 17 IS - 4 SP - 401 EP - 412 N2 - 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. 10.1093/llc/17.4.401 KW - author KW - categorization KW - computational KW - gender KW - lexical KW - syntax KW - technology KW - text AU - Koppel, Moshe AU - Argamon, Shlomo AU - Shimoni, Anat PY - 2002/11/01/ UR - http://dx.doi.org/10.1093/llc/17.4.401 ER -

RIS BibTeX