Using symbolic knowledge in the UMLS to disambiguate words in small datasets with a Naïve Bayes classifier

Studies in Health Technology and Informatics

Volumn 107, Issue , 2004, Pages 381-385

  Leroy, Gondy ^a   Rindflesch, Thomas C ^b  

^a CLAREMONT GRADUATE UNIVERSITY   (United States)

^b NATIONAL INSTITUTES OF HEALTH   (United States)

Author keywords

Artificial intelligence; machine learning; na ve Bayes; small datasets; symbolic knowledge; UMLS; Unified Medical Language System; word sense disambiguation

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLASSIFICATION (OF INFORMATION); DETERIORATION; KNOWLEDGE BASED SYSTEMS; LEARNING ALGORITHMS; NATURAL LANGUAGE PROCESSING SYSTEMS; SEMANTICS; CLASSIFIERS; MACHINE LEARNING;

SMALL DATA SET; SYMBOLIC KNOWLEDGE; UMLS; UNIFIED MEDICAL LANGUAGE SYSTEMS; WORD SENSE DISAMBIGUATION; MACHINE LEARNING TECHNIQUES; MACHINE-LEARNING; NAIVE BAYES; NAIVE BAYES CLASSIFIERS; SEMANTIC TYPES;

LEARNING SYSTEMS; DETERIORATION;

ARTIFICIAL INTELLIGENCE; BAYESIAN LEARNING; CLASSIFIER; CONFERENCE PAPER; CROSS VALIDATION; DETERIORATION; FOLLOW UP; HUMAN; KNOWLEDGE BASE; MACHINE LEARNING; UNIFIED MEDICAL LANGUAGE SYSTEM;

EID: 84887084077     PISSN: 09269630     EISSN: 18798365     Source Type: Book Series    
DOI: 10.3233/978-1-60750-949-3-381     Document Type: Article

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