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dc.contributor.advisorFuruta, Richard
dc.creatorAudenaert, Michael Neal
dc.date.accessioned2006-04-12T16:06:28Z
dc.date.available2006-04-12T16:06:28Z
dc.date.created2005-12
dc.date.issued2006-04-12
dc.identifier.urihttp://hdl.handle.net/1969.1/3317
dc.description.abstractLarge digital libraries typically contain large collections of heterogeneous resources intended to be delivered to a variety of user communities. One key challenge for these libraries is providing tight integration between resources both within a single collection and across the several collections of the library with out requiring hand coding. One key tool in doing this is elucidating the internal structure of the digital resources and using that structure to form connections between the resources. The heterogeneous nature of the collections and the diversity of the needs in the user communities complicates this task. Accordingly, in this thesis, I describe an approach to implementing a feature identification system to support digital collections that provides a general framework for applications while allowing decisions about the details of document representation and features identification to be deferred to domain specific implementations of that framework. These deferred decisions include details of the semantics and syntax of markup, the types of metadata to be attached to documents, the types of features to be identified, the feature identification algorithms to be applied, and which features should be indexed. This approach results in strong support for the general aspects of developing a feature identification system allowing future work to focus on the details of applying that system to the specific needs of individual collections and user communities.en
dc.format.extent780072 bytesen
dc.format.mediumelectronicen
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherTexas A&M University
dc.subjecthumanities informaticsen
dc.subjecthumanities computingen
dc.subjectcollection enhancementen
dc.subjectfeature identificationen
dc.subjectnamed entity recognitionen
dc.titleFeature identification framework and applications (FIFA)en
dc.typeBooken
dc.typeThesisen
thesis.degree.departmentComputer Scienceen
thesis.degree.disciplineComputer Scienceen
thesis.degree.grantorTexas A&M Universityen
thesis.degree.nameMaster of Scienceen
thesis.degree.levelMastersen
dc.contributor.committeeMemberImhoff, Brian
dc.contributor.committeeMemberLeggett, John
dc.contributor.committeeMemberUrbina, Eduardo
dc.type.genreElectronic Thesisen
dc.type.materialtexten
dc.format.digitalOriginborn digitalen


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