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Title: DeepMeSH: Deep Semantic Representation for Improving Large-scale MeSH Indexing.
Authors: Peng, Shengwen
You, Ronghui
Wang, Hongning
Zhai, Chengxiang
Mamitsuka, Hiroshi  kyouindb  KAKEN_id  orcid (unconfirmed)
Zhu, Shanfeng
Author's alias: 馬見塚, 拓
Issue Date: 11-Jun-2016
Publisher: Oxford University Press (OUP)
Journal title: Bioinformatics
Volume: 32
Issue: 12
Start page: i70
End page: i78
Abstract: Motivation: Medical Subject Headings (MeSH) indexing, which is to assign a set of MeSH main headings to citations, is crucial for many important tasks in biomedical text mining and information retrieval. Large-scale MeSH indexing has two challenging aspects: the citation side and MeSH side. For the citation side, all existing methods, including Medical Text Indexer (MTI) by National Library of Medicine and the state-of-the-art method, MeSHLabeler, deal with text by bag-of-words, which cannot capture semantic and context-dependent information well. Methods: We propose DeepMeSH that incorporates deep semantic information for large-scale MeSH indexing. It addresses the two challenges in both citation and MeSH sides. The citation side challenge is solved by a new deep semantic representation, D2V-TFIDF, which concatenates both sparse and dense semantic representations. The MeSH side challenge is solved by using the ‘learning to rank’ framework of MeSHLabeler, which integrates various types of evidence generated from the new semantic representation. Results: DeepMeSH achieved a Micro F-measure of 0.6323, 2% higher than 0.6218 of MeSHLabeler and 12% higher than 0.5637 of MTI, for BioASQ3 challenge data with 6000 citations.
Description: Proceedings of the 24th International Conference on Intelligent Systems for Molecular Biology (ISMB 2016)
Rights: © The Author 2016. Published by Oxford University Press
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( ), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact
DOI(Published Version): 10.1093/bioinformatics/btw294
PubMed ID: 27307646
Appears in Collections:Journal Articles

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