Automatic question generation : a syntactical approach to the sentence-to-question generation case

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dc.contributor.authorAli, Husam Deeb Abdullah Deeb
dc.contributor.supervisorChali, Yllias
dc.date.accessioned2013-04-16T21:18:10Z
dc.date.available2013-04-16T21:18:10Z
dc.date.issued2012
dc.degree.levelMasters
dc.descriptionx, 125 leaves : ill. ; 29 cmen_US
dc.description.abstractHumans are not often very skilled in asking good questions because of their inconsistent mind in certain situations. Thus, Question Generation (QG) and Question Answering (QA) became the two major challenges for the Natural Language Processing (NLP), Natural Language Generation (NLG), Intelligent Tutoring System, and Information Retrieval (IR) communities, recently. In this thesis, we consider a form of Sentence-to-Question generation task where given a sentence as input, the QG system would generate a set of questions for which the sentence contains, implies, or needs answers. Since the given sentence may be a complex sentence, our system generates elementary sentences from the input complex sentences using a syntactic parser. A Part of Speech (POS) tagger and a Named Entity Recognizer (NER) are used to encode necessary information. Based on the subject, verb, object and preposition information, sentences are classified in order to determine the type of questions to be generated. We conduct extensive experiments on the TREC-2007 (Question Answering Track) dataset. The scenario for the main task in the TREC-2007 QA track was that an adult, native speaker of English is looking for information about a target of interest. Using the given target, we filter out the important sentences from the large sentence pool and generate possible questions from them. Once we generate all the questions from the sentences, we perform a recall-based evaluation. That is, we count the overlap of our system generated questions with the given questions in the TREC dataset. For a topic, we get a recall 1.0 if all the given TREC questions are generated by our QG system and 0.0 if opposite. To validate the performance of our QG system, we took part in the First Question Generation Shared Task Evaluation Challenge, QGSTEC in 2010. Experimental analysis and evaluation results along with a comparison of different participants of QGSTEC'2010 show potential significance of our QG system.en_US
dc.identifier.urihttps://hdl.handle.net/10133/3250
dc.language.isoen_USen_US
dc.publisherLethbridge, Alta. : University of Lethbridge, Dept. of Mathematics and Computer Science, c2012en_US
dc.publisher.departmentDepartment of Mathematics and Computer Scienceen_US
dc.publisher.facultyArts and Scienceen_US
dc.relation.ispartofseriesThesis (University of Lethbridge. Faculty of Arts and Science)en_US
dc.subjectQuestion-answering systemsen_US
dc.subjectNatural language processing (Computer science)en_US
dc.titleAutomatic question generation : a syntactical approach to the sentence-to-question generation caseen_US
dc.typeThesisen_US
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