Probabilistic Anomaly Detection Method for Authorship Verification

Abstract : 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.
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Springer International Publishing. 2nd International Conference on Statistical Language and Speech Processing, SLSP 2014, Oct 2014, Grenoble, France. Statistical Language and Speech Processing, 8791, pp.211-219, 2014, Lecture Notes in Computer Science. 〈10.1007/978-3-319-11397-5_16〉
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Contributeur : Mohamed Amine Boukhaled <>
Soumis le : samedi 12 septembre 2015 - 12:37:18
Dernière modification le : mercredi 29 novembre 2017 - 16:32:59
Document(s) archivé(s) le : mardi 29 décembre 2015 - 00:53:06

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Mohamed Amine Boukhaled, Jean-Gabriel Ganascia. Probabilistic Anomaly Detection Method for Authorship Verification. Springer International Publishing. 2nd International Conference on Statistical Language and Speech Processing, SLSP 2014, Oct 2014, Grenoble, France. Statistical Language and Speech Processing, 8791, pp.211-219, 2014, Lecture Notes in Computer Science. 〈10.1007/978-3-319-11397-5_16〉. 〈hal-01198401〉

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