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
Authorship attribution in the wild
Language Resources and Evaluation
Explanation in computational stylometry
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume 2
Syntactic dependency-based n-grams as classification features
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Computational Intelligence - Volume Part II
Detecting machine-morphed malware variants via engine attribution
Journal in Computer Virology
Syntactic N-grams as machine learning features for natural language processing
Expert Systems with Applications: An International Journal
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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.