期刊
JOURNAL OF BIOMEDICAL INFORMATICS
卷 42, 期 5, 页码 814-823出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jbi.2008.12.007
关键词
Abstracting and Indexing as Topic/methods/statistics & numerical data; Artificial Intelligence; Dictionaries, Medical; Evaluation Studies as Topic; MEDLINE; Medical Subject Headings; Natural Language Processing
资金
- NIH
- National Library of Medicine
- Oak Ridge Institute for Science and Education
The volume of biomedical literature has experienced explosive growth in recent years. This is reflected in the corresponding increase in the size of MEDLINE(R), the largest bibliographic database of biomedical citations. Indexers at the US National Library of Medicine (NLM) need efficient tools to help them accommodate the ensuing workload. After reviewing issues in the automatic assignment of Medical Subject Headings (MeSH(R) terms) to biomedical text, we focus more specifically on the new subheading attachment feature for NLM's Medical Text Indexer (MTI). Natural Language Processing, statistical, and machine learning methods of producing automatic MeSH main heading/subheading pair recommendations were assessed independently and combined. The best combination achieves 48% precision and 30% recall. After validation by NLM indexers, a suitable combination of the methods presented in this paper was integrated into MTI as a subheading attachment feature producing MeSH indexing recommendations compliant with current state-of-the-art indexing practice. (C) 2008 Elsevier Inc. All rights reserved.
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