Author Identification Using a Tensor Space Representation

8 years 4 months ago
Author Identification Using a Tensor Space Representation
Author identification is a text categorization task with applications in intelligence, criminal law, computer forensics, etc. Usually, in such cases there is shortage of training texts. In this paper, we propose the use of second order tensors for representing texts for this problem, in contrast to the traditional vector space model. Based on a generalization of the SVM algorithm that can handle tensors, we explore various methods for filling the matrix of features taking into account that similar features should be placed in the same neighborhood. To this end, we propose a frequency-based metric. Experiments on a corpus controlled for genre and topic and variable amount of training texts show that the proposed approach is more effective than traditional vector-based SVM when only limited amount of training texts is used.
Spyridon Plakias, Efstathios Stamatatos
Added 19 Oct 2010
Updated 19 Oct 2010
Type Conference
Year 2008
Where ECAI
Authors Spyridon Plakias, Efstathios Stamatatos
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