{"id":{"repo_id":"texas-state","oai_identifier":"oai:digital.library.txst.edu:10877/8816"},"canonical_url":"https://search.dev.ndltd.org/etd/texas-state/oai:digital.library.txst.edu:10877/8816","repository":{"repo_id":"texas-state","name":"Texas State University","base_url":"https://digital.library.txst.edu/server/oai/request"},"display":{"title":"Similarity Detection Based on Semantic Distance","abstract":"Combining 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. 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