NDLI: Authorship attribution of ancient texts written by ten Arabic travelers using character N-Grams

Content Provider	IEEE Xplore Digital Library
Author	Ouamour, S. Sayoud, H.
Copyright Year	2013
Description	Author affiliation: USTHB Univ., Algiers, Algeria (Ouamour, S.; Sayoud, H.)
Abstract	In this paper the authors investigate the authorship of some old Arabic books that are written by ten ancient Arabic travelers. Hence, several experiments of authorship attribution are conducted on these Arabic texts, by using different features such as characters, character-bigrams, character-trigrams and character-tetragrams. Furthermore, four different classifiers are employed, namely: Stamatatos distance, Manhattan distance, Multi Layer Perceptron (MLP) and Support Vector Machines (SVM). For the evaluation task, several experiments of authorship attribution, using those features and classifiers, are conducted on the Arabic dataset (called AAAT), which contains 3 short texts from every book. Results show good authorship attribution performances with an optimal score of 90% of good attribution. Moreover, this investigation has revealed interesting results concerning the Arabic language.
Sponsorship	IEEE Commun. Soc.
Starting Page	1
Ending Page	5
File Size	281417
Page Count	5
File Format	PDF
e-ISBN	9781479901685
DOI	10.1109/CITS.2013.6705713
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-05-07
Publisher Place	Greece
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Accuracy Text-mining Support vector machine classification Artificial Intelligence Character N-Grams Educational institutions Authorship attribution Arabic language Marine vehicles Testing
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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

Authorship Attribution of Short Historical Arabic Texts Based on Lexical Features

Text Message Authorship Classification Using Kernel Support Vector Machines

Arabie text classification using Learning Vector Quantization

Local histograms of character n-grams for authorship attribution.

Cross-Language Authorship Attribution

Language independent authorship attribution using character level language models (2003).

Local histograms of character n-grams for authorship attribution (2011)

Authorship Attribution in Arabic using a hybrid of evolutionary search and linear discriminant analysis

Authorship attribution of ancient texts written by ten Arabic travelers using character N-Grams

Similar Documents

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

Authorship Attribution of Short Historical Arabic Texts Based on Lexical Features

Text Message Authorship Classification Using Kernel Support Vector Machines

Arabie text classification using Learning Vector Quantization

Local histograms of character n-grams for authorship attribution.

Cross-Language Authorship Attribution

Language independent authorship attribution using character level language models (2003).

Local histograms of character n-grams for authorship attribution (2011)

Authorship Attribution in Arabic using a hybrid of evolutionary search and linear discriminant analysis

Authorship attribution of ancient texts written by ten Arabic travelers using character N-Grams