An incremental approach to MEDLINE MeSH indexing

As an increasing number of new journal articles being added to the MEDLINE database each year, it becomes imperative to build effective systems that can automatically suggest Medical Subject Headings (MeSH) to reduce effort from human annotators. In this paper, we propose three approaches, one building upon another in an incremental way, to automatic MeSH term suggestion: 1) MetaMap-based labeling, which relies on the MetaMap tool to detect MeSH-related concepts for indexing; 2) Search-based labeling, which builds on MetaMap-based approach and further leverages information retrieval techniques for finding similar articles whose existing annotations are used for MeSH suggestion; 3) LLDA-based labeling, which further trains a multi-label classifier based on MeSH ontology for MeSH candidate list pruning. The evaluation on the BioASQ challenge data shows promising results.