PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Measuring differentiability: unmasking pseudonymous authors
Moshe Koppel, Jonathan Schler and Elisheva Bonchek-Dokow
JMLR Volume 8, Number June, pp. 1261-1276, 2007.

Abstract

In the authorship verification problem, we are given examples of the writing of a single author and are asked to determine if given long texts were or were not written by this author. We present a new learning-based method for adducing the "depth of difference" between two example sets and offer evidence that this method solves the authorship verification problem with very high accuracy. The underlying idea is to test the rate of degradation of the accuracy of learned models as the best features are iteratively dropped from the learning process.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Information Retrieval & Textual Information Access
ID Code:3399
Deposited By:Moshe Koppel
Deposited On:10 February 2008