Authorship attribution of ancient texts written by ten arabic travelers using a SMO-SVM classifier

Author

Ouamour, Siham ; Sayoud, Halim

Author_Institution

USTHB Univ., Algiers, Algeria

fYear

2012

fDate

26-28 June 2012

Firstpage

44

Lastpage

47

Abstract

In this paper the authors investigate the task of authorship attribution on very old Arabic texts that were written by ten ancient Arabic travelers. Several features such as characters n-grams and word n-grams are used as input of a SMO-SVM (i.e. Sequential Minimal Optimization based Support Vector Machine). Experiments of authorship attribution, on this text database, show interesting results with a classification precision of 80%. This research work, which represents a rare text-mining work on the Arabic language, has revealed several interesting points.

Keywords

data mining; history; pattern classification; support vector machines; text analysis; Arabic travelers; SMO-SVM classifier; ancient texts; authorship attribution; characters n-grams; sequential minimal optimization based support vector machine; text database; text-mining work; word n-grams; Conferences; Databases; Educational institutions; Pragmatics; Support vector machines; Testing; Training; Artificial Intelligence; Authorship attribution; Data-mining; SVM; Text-mining;

fLanguage

English

Publisher

ieee

Conference_Titel

Communications and Information Technology (ICCIT), 2012 International Conference on

Conference_Location

Hammamet

Print_ISBN

978-1-4673-1949-2

Type

conf

DOI

10.1109/ICCITechnol.2012.6285841

Filename

6285841