dc.contributor.author	Handosa, Mohamed	en
dc.date.accessioned	2014-05-09T13:57:58Z	en
dc.date.available	2014-05-09T13:57:58Z	en
dc.date.issued	2014-05-09	en
dc.description	Short title: Qatar content classification. Long title: Develop methods and software for classifying Arabic texts into a taxonomy using machine learning. Contact person and their contact information: Tarek Kanan, tarekk@vt.edu. Project description: Starting 4/1/2012, and running through 12/31/2015, is a project to advance digital libraries in the country of Qatar. This is led by VT, but also involves Penn State, Texas A&M, and Qatar University. Tarek is a GRA on this effort. His dissertation focuses on classifying Arabic texts into a taxonomy using machine learning. This will be done first for news, and then for other content areas. Project deliverables: Arabic collections, taxonomies, classifiers, and results from experiments to find the best methods. Support: Qatar National Research Fund Project No. NPRP 4-029-1-007	en
dc.description.abstract	This reports on a term project for the CS660 Digital libraries course (Spring 2014). The project has been conducted under the supervision of Prof. Edward Fox and Mr. Tarek Kanan. The goal is to develop an Arabic newspaper article classifier. We have built a collection of 700 Arabic newspaper articles and 1700 Arabic full-newspaper PDF files. A stemmer, named “P-Stemmer”, is proposed. Evaluation have shown that P-Stemmer outperforms Larkey’s widely used light stemmer. Several classification techniques were tested on Arabic data including SVM, Naïve Bayes and Random Forest. We built and tested 21 multiclass classifiers, 15 binary classifiers, and 5 compound classifiers using the voting technique. Finally, we uploaded the classified instances to Apache Solr for searching and indexing purposes.	en
dc.description.sponsorship	Tarek Kanan	en
dc.description.sponsorship	Support has been provided through Qatar National Research Fund Project No. NPRP 4-029-1-007	en
dc.identifier.uri	http://hdl.handle.net/10919/47934	en
dc.language.iso	en_US	en
dc.rights	In Copyright	en
dc.rights.uri	http://rightsstatements.org/vocab/InC/1.0/	en
dc.subject	Qatar	en
dc.subject	Classification	en
dc.subject	SOLR	en
dc.subject	Weka	en
dc.subject	Arabic	en
dc.subject	Machine learning	en
dc.title	Qatar content classification	en
dc.type	Presentation	en
dc.type	Technical report	en

Qatar content classification

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