Show simple item record

dc.contributor.authorKan'an, Tarek Ghazeen_US
dc.date.accessioned2017-01-12T07:00:33Z
dc.date.available2017-01-12T07:00:33Z
dc.date.issued2015-07-21en_US
dc.identifier.othervt_gsexam:5397en_US
dc.identifier.urihttp://hdl.handle.net/10919/74272
dc.description.abstractArabic news articles in heterogeneous electronic collections are difficult for users to work with. Two problems are: that they are not categorized in a way that would aid browsing, and that there are no summaries or detailed metadata records that could be easier to work with than full articles. To address the first problem, schema mapping techniques were adapted to construct a simple taxonomy for Arabic news stories that is compatible with the subject codes of the International Press Telecommunications Council. So that each article would be labeled with the proper taxonomy category, automatic classification methods were researched, to identify the most appropriate. Experiments showed that the best features to use in classification resulted from a new tailored stemming approach (i.e., a new Arabic light stemmer called P-Stemmer). When coupled with binary classification using SVM, the newly developed approach proved to be superior to state-of-the-art techniques. To address the second problem, i.e., summarization, preliminary work was done with English corpora. This was in the context of a new Problem Based Learning (PBL) course wherein students produced template summaries of big text collections. The techniques used in the course were extended to work with Arabic news. Due to the lack of high quality tools for Named Entity Recognition (NER) and topic identification for Arabic, two new tools were constructed: RenA for Arabic NER, and ALDA for Arabic topic extraction tool (using the Latent Dirichlet Algorithm). Controlled experiments with each of RenA and ALDA, involving Arabic speakers and a randomly selected corpus of 1000 Qatari news articles, showed the tools produced very good results (i.e., names, organizations, locations, and topics). Then the categorization, NER, topic identification, and additional information extraction techniques were combined to produce approximately 120,000 summaries for Qatari news articles, which are searchable, along with the articles, using LucidWorks Fusion, which builds upon Solr software. Evaluation of the summaries showed high ratings based on the 1000-article test corpus. Contributions of this research with Arabic news articles thus include a new: test corpus, taxonomy, light stemmer, classification approach, NER tool, topic identification tool, and template-based summarizer – all shown through experimentation to be highly effective.en_US
dc.format.mediumETDen_US
dc.publisherVirginia Techen_US
dc.rightsThis Item is protected by copyright and/or related rights. Some uses of this Item may be deemed fair and permitted by law even without permission from the rights holder(s), or the rights holder(s) may have licensed the work for use under certain conditions. For other uses you need to obtain permission from the rights holder(s).en_US
dc.subjectClassificationen_US
dc.subjectSummarizationen_US
dc.subjectArabic Languageen_US
dc.subjectNatural Language Processingen_US
dc.subjectDigital Librariesen_US
dc.titleArabic News Text Classification and Summarization: A Case of the Electronic Library Institute SeerQ (ELISQ)en_US
dc.typeDissertationen_US
dc.contributor.departmentComputer Scienceen_US
dc.description.degreePh. D.en_US
thesis.degree.namePh. D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineComputer Science and Applicationsen_US
dc.contributor.committeechairFox, Edward Alanen_US
dc.contributor.committeememberAl-Shalabi, Riyaden_US
dc.contributor.committeememberFan, Weiguoen_US
dc.contributor.committeememberShaffer, Clifford A.en_US
dc.contributor.committeememberEhrich, Roger W.en_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record