MeSH Now: automatic MeSH indexing at PubMed scale via learning to rank

• Published in: Journal of biomedical semantics Vol. 8; no. 1; pp. 15 - 9
• Main Authors: Mao, Yuqing, Lu, Zhiyong
• Format: Journal Article
• Language: English
• Published: England BioMed Central 17.04.2017; BMC
• Subjects: Abstracting and Indexing - methods; Artificial intelligence; Automation; Benchmarking; Bibliographic data bases; Bioinformatics; Citation analysis; Classification; Computers; Documents; Gene expression; Indexing; Inductive Logic Programming; Information retrieval; International conferences; Language; Learning; Logic programming; Machine Learning; Medical Subject Headings; Medical Subject Headings-MeSH; MeSH Term; Neural networks; Parameter estimation; Post-processing; PubMed; Query expansion; R&D; Research & development; Semantics; Subject heading schemes; Target Article; Text Classification; Unify Medical Language System; Word sense disambiguation
• ISSN: 2041-1480; 2041-1480
• DOI: 10.1186/s13326-017-0123-3

MeSH indexing is the task of assigning relevant MeSH terms based on a manual reading of scholarly publications by human indexers. The task is highly important for improving literature retrieval and many other scientific investigations in biomedical research. Unfortunately, given its manual nature, the process of MeSH indexing is both time-consuming (new articles are not immediately indexed until 2 or 3 months later) and costly (approximately ten dollars per article). In response, automatic indexing by computers has been previously proposed and attempted but remains challenging. In order to advance the state of the art in automatic MeSH indexing, a community-wide shared task called BioASQ was recently organized. We propose MeSH Now, an integrated approach that first uses multiple strategies to generate a combined list of candidate MeSH terms for a target article. Through a novel learning-to-rank framework, MeSH Now then ranks the list of candidate terms based on their relevance to the target article. Finally, MeSH Now selects the highest-ranked MeSH terms via a post-processing module. We assessed MeSH Now on two separate benchmarking datasets using traditional precision, recall and F -score metrics. In both evaluations, MeSH Now consistently achieved over 0.60 in F-score, ranging from 0.610 to 0.612. Furthermore, additional experiments show that MeSH Now can be optimized by parallel computing in order to process MEDLINE documents on a large scale. We conclude that MeSH Now is a robust approach with state-of-the-art performance for automatic MeSH indexing and that MeSH Now is capable of processing PubMed scale documents within a reasonable time frame. http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/MeSHNow/ .