By Topic

Automatic source attribution of text: a neural networks approach

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Khosmood, F. ; Dept. of Comput. Sci., California Polytech. State Univ., San Luis Obispo, CA, USA ; Kurfess, F.

Recent advances in automatic authorship attribution have been promising. Relatively new techniques such as N-gram analysis have shown important improvements in accuracy by A.P. Engelbrecht (2002). Much of the work in this area does remain in the realm of statistics best suited for human assistance rather than autonomous attribution in "computer and humanities" by N. Fakotakis et al (2001). While there have been attempts at using neural networks in the area in the past, they have been extremely limited and problem-specific in "proceedings EACL" by N. Fakotakis et al (1999). This paper addresses the latter points by demonstrating a practical and truly autonomous attribution process using neural networks. Furthermore, we use a word frequency classification technique to demonstrate the feasibility of this process in particular and the applications of neural networks to textual analysis in general.

Published in:

Neural Networks, 2005. IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on  (Volume:5 )

Date of Conference:

31 July-4 Aug. 2005