Measurement of Semantic Textual Similarity in Clinical Texts: Comparison of Transformer-Based Models
Xi Yang; Xing He; Hansi Zhang; Yinghan Ma; Jiang Bian; Yonghui Wu · 2020 · JMIR Medical Informatics
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
BACKGROUND: Semantic textual similarity (STS) is one of the fundamental tasks in natural language processing (NLP). Many shared tasks and corpora for STS have been organized and curated in the general English domain; however, such resources are limited in the biomedical domain. In 2019, the National NLP Clinical Challenges (n2c2) challenge developed a comprehensive clinical STS dataset and organized a community effort to solicit state-of-the-art solutions for clinical STS. OBJECTIVE: This study presents our transformer-based clinical STS models developed during this challenge as well as new mo
Abstract by Xi Yang; Xing He; Hansi Zhang; Yinghan Ma; Jiang Bian; Yonghui Wu, JMIR Medical Informatics (2020) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.2196/19735