Authorship Attribution of Specific Arabic Texts using Character N-gram and Classification Techniques

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dc.contributor.authorBELDJOUDI Bochra
dc.contributor.authorGAHAM Sabrina
dc.contributor.authorENCA/KHENNOUF Salah
dc.date.accessioned2024-07-10T09:07:03Z
dc.date.available2024-07-10T09:07:03Z
dc.date.issued2024-07-10
dc.description.abstractThe written word has always been a cornerstone of civilizations, essential for preserving and transmitting knowledge. Stylometry, at the intersection of linguistics and statistics, aims to identify a text's style, inherent to its author, as well as its era and genre. This technique analyzes anonymous texts to recognize their authors and applies to both ancient and modern texts. This work aims to identify the author of a specific Arabic text and test the robustness of an author recognition system. Features such as character N-Grams and classification techniques are used.
dc.identifier.otherEL/2024
dc.identifier.urihttps://dspace.univ-msila.dz/handle/123456789/43510
dc.publisherUNIVERSITY OF MOHAMED BOUDIAF-M’SILA
dc.subjectStylometry
dc.subjectAuthorship Attribution
dc.subjectN-Grams Character
dc.subjectMachine Learning
dc.titleAuthorship Attribution of Specific Arabic Texts using Character N-gram and Classification Techniques
dc.typeThesis

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