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

Tutkimustuotos: Lehtiartikkeli

Tutkijat

Organisaatiot

  • Fudan University
  • University of Virginia
  • University of Illinois at Urbana-Champaign
  • Bioinformatics Center
  • Kyoto University

Kuvaus

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.

Yksityiskohdat

AlkuperäiskieliEnglanti
Sivuti70-i79
Sivumäärä10
JulkaisuBioinformatics
Vuosikerta32
Numero12
TilaJulkaistu - 15 kesäkuuta 2016
OKM-julkaisutyyppiA1 Julkaistu artikkeli, soviteltu
TapahtumaInternational Conference on Intelligent Systems for Molecular Biology - Orlando, Yhdysvallat
Kesto: 8 heinäkuuta 201612 heinäkuuta 2016
Konferenssinumero: 24
https://www.iscb.org/ismb2016

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