Probabilistic Anomaly Detection Method for Authorship Verification

Mohamed Amine Boukhaled (1) , Jean-Gabriel Ganascia (1)

1 ACASA - Agents Cognitifs et Apprentissage Symbolique Automatique

Authorship verification is the task of determining if a given text is written by a candidate author or not. In this paper, we present a first study on using an anomaly detection method for the authorship verification task. We have considered a weakly supervised probabilistic model based on a multivari-ate Gaussian distribution. To evaluate the effectiveness of the proposed method, we conducted experiments on a classic French corpus. Our preliminary results show that the probabilistic method can achieve a high verification performance that can reach an F 1 score of 85%. Thus, this method can be very valuable for authorship verification.

Mots clés: Authorship verification; anomaly detection; multivariate Gaussian distribution

Domaines: Traitement du texte et du document; Intelligence artificielle [cs.AI]; Base de données [cs.DB]; Apprentissage [cs.LG]; Machine Learning [stat.ML]

Mohamed Amine Boukhaled, Jean-Gabriel Ganascia. Probabilistic Anomaly Detection Method for Authorship Verification. 2nd International Conference on Statistical Language and Speech Processing, SLSP 2014, Oct 2014, Grenoble, France. pp.211-219, ⟨10.1007/978-3-319-11397-5_16⟩. ⟨hal-01198401⟩