Table Understanding for Information Retrieval

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dc.contributor.authorPande, Ashwini K.en
dc.contributor.committeechairEhrich, Roger W.en
dc.contributor.committeememberFox, Edward A.en
dc.contributor.committeememberNorth, Christopher L.en
dc.contributor.departmentComputer Scienceen
dc.date.accessioned2014-03-14T20:44:23Zen
dc.date.adate2002-09-03en
dc.date.available2014-03-14T20:44:23Zen
dc.date.issued2002-08-19en
dc.date.rdate2003-09-03en
dc.date.sdate2002-08-28en
dc.description.abstractThis thesis proposes a novel approach for finding tables in text files containing a mixture of unstructured and structured text. Tables may be arbitrarily complex because the data in the tables may themselves be tables and because the grouping of data elements displayed in a table may be very complex. Although investigators have proposed competence models to explain the structure of tables, there are no computationally feasible performance models for detecting and parsing general structures in real data. Our emphasis is placed on the investigation of a new statistical procedure for detecting basic tables in plain text documents. The main task here is defining and testing this theory in the context of the Odessa Digital Library.en
dc.description.degreeMaster of Scienceen
dc.identifier.otheretd-08282002-151909en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-08282002-151909/en
dc.identifier.urihttp://hdl.handle.net/10919/34820en
dc.publisherVirginia Techen
dc.relation.haspartAshwiniPandeTableIR.pdfen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectInformation retrievalen
dc.subjectStatistical crosscorrelationen
dc.subjectOdessa digital libraryen
dc.subjectdetection heuristicsen
dc.subjectTable detectionen
dc.titleTable Understanding for Information Retrievalen
dc.typeThesisen
thesis.degree.disciplineComputer Scienceen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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