This paper is in the following e-collection/theme issue:

Natural Language Processing (637) AI Language Models in Health Care (6) Big Data (216) Machine Learning (1373) Theme issue 2019: Health Natural Language Processing (17) Tools, Programs and Algorithms (237) New Methods (902) Ontologies, Classifications, and Coding (224) Development and Evaluation of Research Methods, Instruments and Tools (423)

Published on in Vol 12 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/59680, first published .
Is Boundary Annotation Necessary? Evaluating Boundary-Free Approaches to Improve Clinical Named Entity Annotation Efficiency: Case Study

Is Boundary Annotation Necessary? Evaluating Boundary-Free Approaches to Improve Clinical Named Entity Annotation Efficiency: Case Study

Is Boundary Annotation Necessary? Evaluating Boundary-Free Approaches to Improve Clinical Named Entity Annotation Efficiency: Case Study

Authors of this article:

Gabriel Herman Bernardim Andrade1 Author Orcid Image ;   Shuntaro Yada1 Author Orcid Image ;   Eiji Aramaki1 Author Orcid Image
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Gabriel Herman Bernardim Andrade   1 , MSc ;   Shuntaro Yada   1 , PhD ;   Eiji Aramaki   1 , PhD

1 Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan

Corresponding Author:

  • Eiji Aramaki, PhD
  • Graduate School of Science and Technology
  • Nara Institute of Science and Technology
  • 8916-5, Takayama-cho
  • Ikoma, 630-0192
  • Japan
  • Phone: 81 743-72-5250
  • Email: aramaki@is.naist.jp