Université Blida 1

Authorship Verification From Text

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dc.contributor.author Mahi, Abdelkrim
dc.contributor.author Menouer, Ramzi
dc.date.accessioned 2021-09-15T11:21:38Z
dc.date.available 2021-09-15T11:21:38Z
dc.date.issued 2021
dc.identifier.uri http://di.univ-blida.dz:8080/jspui/handle/123456789/11918
dc.description ill., Bibliogr. fr_FR
dc.description.abstract Authorship verification (AV) is one of various topics parts of authorship analysis field that deals with the problem of determine whether two texts were written by the same author or not. A combination of a similarity-based methods and relevant linguistic features are used to achieve high accuracy authorship verification. To address this problem, we proposed a new approach using the Autoencoder deep learning method. Challenges in the context of Authorship verification have greatly increased in recent years, as the challenge of PAN (series of scientific events) for three last years from 2020 to 2022. To experiment our approach, we used the data provided by PAN 2021 AV task. Keywords: Authorship verification, PAN, Autoencoder, deep learning, similarity. fr_FR
dc.language.iso en fr_FR
dc.publisher Université Blida 1 fr_FR
dc.subject Authorship verification fr_FR
dc.subject PAN fr_FR
dc.subject Autoencoder fr_FR
dc.subject deep learning fr_FR
dc.subject similarity fr_FR
dc.title Authorship Verification From Text fr_FR
dc.type Thesis fr_FR


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