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Title: MeSHLabeler and DeepMeSH: Recent Progress in Large-Scale MeSH Indexing
Authors: Peng, Shengwen
Mamitsuka, Hiroshi  kyouindb  KAKEN_id  orcid (unconfirmed)
Zhu, Shanfeng
Author's alias: 馬見塚, 拓
Keywords: MeSH indexing
Text categorization
Multi-label classification
Medical subject headings
Machine learning
Issue Date: 21-Jul-2018
Publisher: Springer New York
Journal title: Methods in Molecular Biology
Volume: 1807
Start page: 203
End page: 209
Abstract: The US National Library of Medicine (NLM) uses the Medical Subject Headings (MeSH) (seeNote 1 ) to index almost all 24 million citations in MEDLINE, which greatly facilitates the application of biomedical information retrieval and text mining. Large-scale automatic MeSH indexing has two challenging aspects: the MeSH side and citation side. For the MeSH side, each citation is annotated by only 12 (on average) out of all 28, 000 MeSH terms. For the citation side, all existing methods, including Medical Text Indexer (MTI) by NLM, deal with text by bag-of-words, which cannot capture semantic and context-dependent information well. To solve these two challenges, we developed the MeSHLabeler and DeepMeSH. By utilizing “learning to rank” (LTR) framework, MeSHLabeler integrates multiple types of information to solve the challenge in the MeSH side, while DeepMeSH integrates deep semantic representation to solve the challenge in the citation side. MeSHLabeler achieved the first place in both BioASQ2 and BioASQ3, and DeepMeSH achieved the first place in both BioASQ4 and BioASQ5 challenges. DeepMeSH is available at
Rights: This is a post-peer-review, pre-copyedit version of an article published in Methods in Molecular Biology. The final authenticated version is available online at: in Molecular Biology.
The full-text file will be made open to the public on 21 July 2019 in accordance with publisher's 'Terms and Conditions for Self-Archiving'.
This is not the published version. Please cite only the published version. この論文は出版社版でありません。引用の際には出版社版をご確認ご利用ください。
DOI(Published Version): 10.1007/978-1-4939-8561-6_15
PubMed ID: 30030813
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