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

Theme Issue 2020:National NLP Clinical Challenges/Open Health Natural Language Processing 2019 Challenge Selected Papers (17) Natural Language Processing (692)

Published on in Vol 8, No 12 (2020): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/23357, first published .
Using Character-Level and Entity-Level Representations to Enhance Bidirectional Encoder Representation From Transformers-Based Clinical Semantic Textual Similarity Model: ClinicalSTS Modeling Study

Using Character-Level and Entity-Level Representations to Enhance Bidirectional Encoder Representation From Transformers-Based Clinical Semantic Textual Similarity Model: ClinicalSTS Modeling Study

Using Character-Level and Entity-Level Representations to Enhance Bidirectional Encoder Representation From Transformers-Based Clinical Semantic Textual Similarity Model: ClinicalSTS Modeling Study

Authors of this article:

Ying Xiong1 Author Orcid Image ;   Shuai Chen1 Author Orcid Image ;   Qingcai Chen1, 2 Author Orcid Image ;   Jun Yan3 Author Orcid Image ;   Buzhou Tang1, 2 Author Orcid Image
Article Authors Cited by (7) Tweetations (3) Metrics

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  4. Kalyan K, Rajasekharan A, Sangeetha S. AMMU: A survey of transformer-based biomedical pretrained language models. Journal of Biomedical Informatics 2022;126:103982 View
  5. Lyu D, Wang X, Chen Y, Wang F. Language model and its interpretability in biomedicine: A scoping review. iScience 2024;27(4):109334 View