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.

• Type: Conference
• Year: 2008
• Where: ECAI
• Authors: Spyridon Plakias, Efstathios Stamatatos
• Keywords: Artificial Intelligence, ECAI 2008, Text Categorization Task, Traditional Vector-based Svm, Training Texts