DeepMeSH: Deep semantic representation for improving large-scale MeSH indexing

Shengwen Peng, Ronghui You, Hongning Wang, Chengxiang Zhai, Hiroshi Mamitsuka, Shanfeng Zhu*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

63 Citations (Scopus)
116 Downloads (Pure)

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.

Original languageEnglish
Pages (from-to)i70-i79
Number of pages10
JournalBioinformatics
Volume32
Issue number12
DOIs
Publication statusPublished - 15 Jun 2016
MoE publication typeA1 Journal article-refereed
EventInternational Conference on Intelligent Systems for Molecular Biology - Orlando, United States
Duration: 8 Jul 201612 Jul 2016
Conference number: 24
https://www.iscb.org/ismb2016

Keywords

  • biomedical text mining
  • Ontology
  • text mining
  • MeSH
  • deep learning

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