Automatic Resolution of Ambiguous Terms Based on Machine Learning and Conceptual Relations in the UMLS | Zendy

Haibo Liu | Zendy

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Automatic Resolution of Ambiguous Terms Based on Machine Learning and Conceptual Relations in the UMLS

Author(s) -

Haibo Liu

Publication year - 2002

Publication title -

journal of the american medical informatics association

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.614

H-Index - 150

eISSN - 1527-974X

pISSN - 1067-5027

DOI - 10.1197/jamia.m1101

Subject(s) - unified medical language system , computer science , natural language processing , set (abstract data type) , information retrieval , artificial intelligence , precision and recall , annotation , recall , domain (mathematical analysis) , task (project management) , linguistics , mathematical analysis , philosophy , mathematics , management , economics , programming language

Motivation. The UMLS has been used in natural language processing applications such as information retrieval and information extraction systems. The mapping of free-text to UMLS concepts is important for these applications. To improve the mapping, we need a method to disambiguate terms that possess multiple UMLS concepts. In the general English domain, machine-learning techniques have been applied to sense-tagged corpora, in which senses (or concepts) of ambiguous terms have been annotated (mostly manually). Sense disambiguation classifiers are then derived to determine senses (or concepts) of those ambiguous terms automatically. However, manual annotation of a corpus is an expensive task. We propose an automatic method that constructs sense-tagged corpora for ambiguous terms in the UMLS using MEDLINE abstracts.

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