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dc.contributor.authorAdams, Kimberly E. ( )
dc.identifier.citationAdams, K. E. (2007). Similarity detection based on semantic distance (Unpublished thesis). Texas State University-San Marcos, San Marcos, Texas.
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.format.extent216 pages
dc.format.medium1 file (.pdf)
dc.subjectConceptual structures
dc.subjectProgramming languages
dc.subjectInformation theory
dc.subjectElectronic computers
dc.titleSimilarity Detection Based on Semantic Distance
txstate.documenttypeThesis Science State University--San Marcos of Science
txstate.departmentComputer Science


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