activity
20192021
most citedRobustly Pre-trained Neural Model for Direct Temporal Relation Extraction

5 citations · 6 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CL20211 cited

An Empirical Study of UMLS Concept Extraction from Clinical Notes using Boolean Combination Ensembles

Greg M. Silverman, Raymond L. Finzel, Michael V. Heinz +10

Our objective in this study is to investigate the behavior of Boolean operators on combining annotation output from multiple Natural Language Processing (NLP) systems across multip…

cs.IR2021

Discovering novel drug-supplement interactions using a dietary supplements knowledge graph generated from the biomedical literature

Dalton Schutte, Jake Vasilakes, Anu Bompelli +7

OBJECTIVE: Leverage existing biomedical NLP tools and DS domain terminology to produce a novel and comprehensive knowledge graph containing dietary supplement (DS) information for…

cs.CL20205 cited

Robustly Pre-trained Neural Model for Direct Temporal Relation Extraction

Hong Guan, Jianfu Li, Hua Xu +1

Background: Identifying relationships between clinical events and temporal expressions is a key challenge in meaningfully analyzing clinical text for use in advanced AI application…

cs.IR2019

BERT-based Ranking for Biomedical Entity Normalization

Zongcheng Ji, Qiang Wei, Hua Xu

Developing high-performance entity normalization algorithms that can alleviate the term variation problem is of great interest to the biomedical community. Although deep learning-b…

cs.CL2019

Enhancing Clinical Concept Extraction with Contextual Embeddings

Yuqi Si, Jingqi Wang, Hua Xu +1

Neural network-based representations ("embeddings") have dramatically advanced natural language processing (NLP) tasks, including clinical NLP tasks such as concept extraction. Rec…