Enhanced word representations for bridging anaphora resolution
Yufang Hou
NAACL 2018
Due to the fast pace at which randomized controlled trials are published in the health domain, researchers, consultants and policymakers would benefit from more automatic ways to process them by both extracting relevant information and automating the meta-analysis processes. In this paper, we present a novel methodology based on natural language processing and reasoning models to 1) extract relevant information from RCTs and 2) predict potential outcome values on novel scenarios, given the extracted knowledge, in the domain of behavior change for smoking cessation.
Yufang Hou
NAACL 2018
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ChemBioChem
Italo Buleje, Vince Siu, et al.
ICDH 2023
Shashanka Ubaru, Lior Horesh, et al.
Journal of Biomedical Informatics