no code implementations • NAACL (BioNLP) 2021 • William Hogan, Yoshiki Vazquez Baeza, Yannis Katsis, Tyler Baldwin, Ho-Cheol Kim, Chun-Nan Hsu
NLP has emerged as an essential tool to extract knowledge from the exponentially increasing volumes of biomedical texts.
no code implementations • NAACL 2022 • Luyao Shi, Tanveer Syeda-Mahmood, Tyler Baldwin
However, these methods still lack semantic understanding of the underlying clinical conditions as well as ruled out findings, resulting in poor precision during retrieval.
no code implementations • 20 Aug 2022 • Jiacheng Li, Yannis Katsis, Tyler Baldwin, Ho-Cheol Kim, Andrew Bartko, Julian McAuley, Chun-Nan Hsu
To address these problems, we propose a new pre-trained model that learns representations of both entities and relationships from token spans and span pairs in the text respectively.
Ranked #4 on Relation Extraction on SemEval-2010 Task-8
no code implementations • 18 May 2022 • Maysa M. Garcia de Macedo, Wyatt Clarke, Eli Lucherini, Tyler Baldwin, Dilermando Queiroz Neto, Rogerio de Paula, Subhro Das
Rapid technological innovation threatens to leave much of the global workforce behind.
1 code implementation • AKBC 2021 • William Hogan, Molly Huang, Yannis Katsis, Tyler Baldwin, Ho-Cheol Kim, Yoshiki Vazquez Baeza, Andrew Bartko, Chun-Nan Hsu
In this work, we propose a novel reformulation of MIL for biomedical relation extraction that abstractifies biomedical entities into their corresponding semantic types.