By Topic

Probabilistic Text Change Detection Using an Immune Model

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
$33 $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)
Matti Polla ; Adaptive Informatics Research Centre (AIRC), Helsinki University of Technology, P.O. Box 5400 FI-02015, Espoo, Finland. phone +358-9-451-5115; fax: +358-9-451-2711; email: matti.polla@tkk.fi ; Timo Honkela

We present a probabilistic approach for detecting and analyzing changes in natural language motivated by biological immune systems. Contrary to traditional methods based on message-digest algorithms and line-by-line comparisons of two files, the proposed algorithm employs an implicit negative representation of text segments in the form of detector strings. A characteristic property of the presented change detection method is that it allows the analysis to be done without revealing the full contents of the original data to the authenticator. Implications of this property to security applications are outlined and an experiment is conducted to show how several incremental changes to a collaboratively maintained document can be analyzed.

Published in:

2007 International Joint Conference on Neural Networks

Date of Conference:

12-17 Aug. 2007