Tensor Space Models for Authorship Identification
SETN '08 Proceedings of the 5th Hellenic conference on Artificial Intelligence: Theories, Models and Applications
A survey of modern authorship attribution methods
Journal of the American Society for Information Science and Technology
Authorship attribution and verification with many authors and limited data
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Language Resources and Evaluation
Semi-random subspace method for writeprint identification
Neurocomputing
The use of orthogonal similarity relations in the prediction of authorship
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume 2
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This paper deals with the problem of author identification. The Common N-Grams (CNG) method [6] is a language-independent profile-based approach with good results in many author identification experiments so far. A variation of this approach is presented based on new distance measures that are quite stable for large profile length values. Special emphasis is given to the degree upon which the effectiveness of the method is affected by the available training text samples per author. Experiments based on text samples on the same topic from the Reuters Corpus Volume 1 are presented using both balanced and imbalanced training corpora. The results show that CNG with the proposed distance measures is more accurate when only limited training text samples are available, at least for some of the candidate authors, a realistic condition in author identification problems.