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dc.contributor.authorAdams, Kimberly E. ( )
dc.date.accessioned2019-11-16T17:32:17Z
dc.date.available2019-11-16T17:32:17Z
dc.date.issued2007-08
dc.identifier.citationAdams, K. E. (2007). Similarity detection based on semantic distance (Unpublished thesis). Texas State University-San Marcos, San Marcos, Texas.
dc.identifier.urihttps://digital.library.txstate.edu/handle/10877/8816
dc.description.abstractCombining basic language processing methodologies with the conceptual framework of WordNet, semantic distance is used to measure the similarity between documents. Noun and verb word concepts are transformed into paths that represent their physical location within the WordNet hierarchy. Path prefixes are compared using three distinct algorithms-each investigates a particular type of semantic distance. The results suggest that similarity can be derived from a conceptual hierarchy. The key to finding similarity lies in its precise definition.
dc.formatText
dc.format.extent216 pages
dc.format.medium1 file (.pdf)
dc.language.isoen
dc.subjectConceptual structures
dc.subjectProgramming languages
dc.subjectInformation theory
dc.subjectElectronic computers
dc.subjectSemantics
dc.titleSimilarity Detection Based on Semantic Distance
txstate.documenttypeThesis
thesis.degree.departmentComputer Science
thesis.degree.grantorTexas State University--San Marcos
thesis.degree.levelMasters
thesis.degree.nameMaster of Science
txstate.accessrestricted
txstate.departmentComputer Science


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