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

Ying Xiong   1 , PhD ;   Shuai Chen   1 , MS ;   Qingcai Chen   1, 2 , PhD, Prof Dr ;   Jun Yan   3 , PhD ;   Buzhou Tang   1, 2 , PhD, Prof Dr

1 Harbin Institute of Technology, Shenzhen, China

2 Peng Cheng Laboratory, Shenzhen, China

3 Yidu Cloud Technology Company Limited, Beijing, China

Corresponding Author: