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dc.contributor.advisorUlusoy, Özgür
dc.contributor.authorTatar, Serhan
dc.date.accessioned2016-01-08T18:14:29Z
dc.date.available2016-01-08T18:14:29Z
dc.date.issued2011
dc.identifier.urihttp://hdl.handle.net/11693/15166
dc.descriptionAnkara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2011.en_US
dc.descriptionThesis (Ph. D.) -- Bilkent University, 2011.en_US
dc.descriptionIncludes bibliographical references leaves 85-97.en_US
dc.description.abstractThroughout history, mankind has often suffered from a lack of necessary resources. In today’s information world, the challenge can sometimes be a wealth of resources. That is to say, an excessive amount of information implies the need to find and extract necessary information. Information extraction can be defined as the identification of selected types of entities, relations, facts or events in a set of unstructured text documents in a natural language. The goal of our research is to build a system that automatically locates and extracts information from Turkish unstructured texts. Our study focuses on two basic Information Extraction (IE) tasks: Named Entity Recognition and Entity Relation Detection. Named Entity Recognition, finding named entities (persons, locations, organizations, etc.) located in unstructured texts, is one of the most fundamental IE tasks. Entity Relation Detection task tries to identify relationships between entities mentioned in text documents. Using supervised learning strategy, the developed systems start with a set of examples collected from a training dataset and generate the extraction rules from the given examples by using a carefully designed coverage algorithm. Moreover, several rule filtering and rule refinement techniques are utilized to maximize generalization and accuracy at the same time. In order to obtain accurate generalization, we use several syntactic and semantic features of the text, including: orthographical, contextual, lexical and morphological features. In particular, morphological features of the text are effectively used in this study to increase the extraction performance for Turkish, an agglutinative language. Since the system does not rely on handcrafted rules/patterns, it does not heavily suffer from domain adaptability problem. The results of the conducted experiments show that (1) the developed systems are successfully applicable to the Named Entity Recognition and Entity Relation Detection tasks, and (2) exploiting morphological features can significantly improve the performance of information extraction from Turkish, an agglutinative language.en_US
dc.description.statementofresponsibilityTatar, Serhanen_US
dc.format.extentxviii, 110 leavesen_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectInformation Extractionen_US
dc.subjectTurkishen_US
dc.subjectNamed Entity Recognitionen_US
dc.subjectEntity Relation Detectionen_US
dc.subject.lccQA76.9.N38 T38 2011en_US
dc.subject.lcshNatural language processing (Computer science)en_US
dc.subject.lcshComputational linguistics.en_US
dc.subject.lcshInformation storage and retrieval systems.en_US
dc.titleAutomating information extraction task for Turkish textsen_US
dc.typeThesisen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreePh.D.en_US


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