Automatic indexing of documents from journal descriptors: a preliminary investigation
Journal of the American Society for Information Science
Hierarchical Text Categorization Using Neural Networks
Information Retrieval
Stacking Bagged and Dagged Models
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Journal of Biomedical Informatics - Special issue: Unified medical language system
Hierarchical document categorization with support vector machines
Proceedings of the thirteenth ACM international conference on Information and knowledge management
The SMART Retrieval System—Experiments in Automatic Document Processing
The SMART Retrieval System—Experiments in Automatic Document Processing
Automatic indexing of online health resources for a French quality controlled gateway
Information Processing and Management: an International Journal
BioNLP '07 Proceedings of the Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing
Guest Editorial: Current issues in biomedical text mining and natural language processing
Journal of Biomedical Informatics
Journal of the American Society for Information Science and Technology
Automatic indexing and retrieval of encounter-specific evidence for point-of-care support
Journal of Biomedical Informatics
Use of Medical Subject Headings (MeSH) in Portuguese for categorizing web-based healthcare content
Journal of Biomedical Informatics
Web Semantics: Science, Services and Agents on the World Wide Web
Artificial Intelligence in Medicine
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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.