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AUTOMATED KEYWORD EXTRACTION FROM BIO-MEDICAL LITERATURE WITH CONCENTRATION ON ANTIBIOTIC RESISTANCE

dc.contributor.advisorPop, Mihaien_US
dc.contributor.authorZuhl, Mayaen_US
dc.date.accessioned2009-10-06T05:30:13Z
dc.date.available2009-10-06T05:30:13Z
dc.date.issued2009en_US
dc.identifier.urihttp://hdl.handle.net/1903/9442
dc.description.abstractThe explosive growth of bio-medical literature makes it increasingly difficult and time consuming to keep up with newly discovered and published information. The extraction of knowledge from papers is critical in enabling computational analysis of biological data. In the last decade, tremendous effort has been put into development of automated and semi-automated tools for knowledge discovery and extraction from text, as an alternative to monotonous and time-consuming manual processing. This thesis research was focused on determining whether minor human supervision can improve the process of automated bio-medical text annotation. One of the main outcomes of this study is a tool that requires minimal effort and time from scientists to reach high precision in semi-automated annotation. The task we targeted is the extraction of keywords related to antibiotic resistance in bacteria. The tool is based on a machine learning algorithm that is retrained several times to achieve the best accuracy.en_US
dc.format.extent1261378 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.titleAUTOMATED KEYWORD EXTRACTION FROM BIO-MEDICAL LITERATURE WITH CONCENTRATION ON ANTIBIOTIC RESISTANCEen_US
dc.typeThesisen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.contributor.departmentComputer Scienceen_US
dc.subject.pqcontrolledComputer Scienceen_US
dc.subject.pquncontrolledAntibiotic Resistanceen_US
dc.subject.pquncontrolledAutomated Annotationen_US
dc.subject.pquncontrolledbio-medical literatureen_US
dc.subject.pquncontrolledData Miningen_US
dc.subject.pquncontrolledMachine Learningen_US
dc.subject.pquncontrolledSupport Vector Machineen_US


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