{"id":{"repo_id":"unlv","oai_identifier":"oai:oasis.library.unlv.edu:rtds-2613"},"canonical_url":"https://search.dev.ndltd.org/etd/unlv/oai:oasis.library.unlv.edu:rtds-2613","repository":{"repo_id":"unlv","name":"University of Nevada - Las Vegas","base_url":"https://oasis.library.unlv.edu/do/oai/"},"display":{"title":"Thesaurus-aided learning for rule-based categorization of Ocr texts","abstract":"The question posed in this thesis is whether the effectiveness of the rule-based approach to automatic text categorization on OCR collections can be improved by using domain-specific thesauri. A rule-based categorizer was constructed consisting of a C++ program called C-KANT which consults documents and creates a program which can be executed by the CLIPS expert system shell. A series of tests using domain-specific thesauri revealed that a query expansion approach to rule-based automatic text categorization using domain-dependent thesauri will not improve the categorization of OCR texts. Although some improvement to categorization could be made using rules over a mixture of thesauri, the improvements were not significantly large.","abstract_html":"The question posed in this thesis is whether the effectiveness of the rule-based approach to automatic text categorization on OCR collections can be improved by using domain-specific thesauri. A rule-based categorizer was constructed consisting of a C++ program called C-KANT which consults documents and creates a program which can be executed by the CLIPS expert system shell. A series of tests using domain-specific thesauri revealed that a query expansion approach to rule-based automatic text categorization using domain-dependent thesauri will not improve the categorization of OCR texts. 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