A comparison of word embeddings for the biomedical natural language processing.

Journal: Journal of biomedical informatics
Published Date: Nov 1, 2018

Abstract

BACKGROUND: Word embeddings have been prevalently used in biomedical Natural Language Processing (NLP) applications due to the ability of the vector representations being able to capture useful semantic properties and linguistic relationships between words. Different textual resources (e.g., Wikipedia and biomedical literature corpus) have been utilized in biomedical NLP to train word embeddings and these word embeddings have been commonly leveraged as feature input to downstream machine learning models. However, there has been little work on evaluating the word embeddings trained from different textual resources.

Authors

  • Yanshan Wang
    Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
  • Sijia Liu
    These authors contributed equally to this study and Dr. Li is now working at IBM; Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA; Department of Computer Science and Engineering, University at Buffalo, Buffalo, NY, USA.
  • Naveed Afzal
    Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
  • Majid Rastegar-Mojarad
    Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
  • Liwei Wang
    Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
  • Feichen Shen
    Department of Health Sciences Research, Rochester MN.
  • Paul Kingsbury
    Department of Health Sciences Research, Mayo Clinic, Rochester, USA. Electronic address: Kingsbury.Paul1@mayo.edu.
  • Hongfang Liu
    Department of Artificial Intelligence & Informatics, Mayo Clinic, Rochester, MN, United States.

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