Abstract
The task of authorship verification consists in detecting whether two texts have been written by the same person. This paper describes the CLG Authorship Analytics software, which implements several individual methods as well as a stacked generalization system for authorship verification. The approach relies primarily on ensemble learning methods, i.e. repeatedly sampling the data in order to capture the invariant stylistic patterns. The approach is tested through a series of experiments designed to test the ability of the system to generalize, depending on various parameters. The code and results of the experiments are publicly available https://github.com/erwanm/clg-authorship-experiments.
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