2 citations · 2 across the 2 of their papers we have counts for
10 papers
Biomedical Concept Relatedness -- A large EHR-based benchmark
Claudia Schulz, Josh Levy-Kramer, Camille Van Assel +2
A promising application of AI to healthcare is the retrieval of information from electronic health records (EHRs), e.g. to aid clinicians in finding relevant information for a cons…
Can Embeddings Adequately Represent Medical Terminology? New Large-Scale Medical Term Similarity Datasets Have the Answer!
Claudia Schulz, Damir Juric
A large number of embeddings trained on medical data have emerged, but it remains unclear how well they represent medical terminology, in particular whether the close relationship…
A Richly Annotated Corpus for Different Tasks in Automated Fact-Checking
Andreas Hanselowski, Christian Stab, Claudia Schulz +2
Automated fact-checking based on machine learning is a promising approach to identify false information distributed on the web. In order to achieve satisfactory performance, machin…
FAMULUS: Interactive Annotation and Feedback Generation for Teaching Diagnostic Reasoning
Jonas Pfeiffer, Christian M. Meyer, Claudia Schulz +7
Our proposed system FAMULUS helps students learn to diagnose based on automatic feedback in virtual patient simulations, and it supports instructors in labeling training data. Diag…
Analysis of Automatic Annotation Suggestions for Hard Discourse-Level Tasks in Expert Domains
Claudia Schulz, Christian M. Meyer, Jan Kiesewetter +5
Many complex discourse-level tasks can aid domain experts in their work but require costly expert annotations for data creation. To speed up and ease annotations, we investigate th…
Text Processing Like Humans Do: Visually Attacking and Shielding NLP Systems
Steffen Eger, Gözde Gül Şahin, Andreas Rücklé +6
Visual modifications to text are often used to obfuscate offensive comments in social media (e.g., "!d10t") or as a writing style ("1337" in "leet speak"), among other scenarios. W…