13 citations · 26 across the 6 of their papers we have counts for
11 papers · 1 filter
COVID-SEE: Scientific Evidence Explorer for COVID-19 Related Research
Karin Verspoor, Simon Šuster, Yulia Otmakhova +7
We present COVID-SEE, a system for medical literature discovery based on the concept of information exploration, which builds on several distinct text analysis and natural language…
Distilling neural networks into skipgram-level decision lists
Madhumita Sushil, Simon Šuster, Walter Daelemans
Several previous studies on explanation for recurrent neural networks focus on approaches that find the most important input segments for a network as its explanations. In that cas…
Why can't memory networks read effectively?
Simon Šuster, Madhumita Sushil, Walter Daelemans
Memory networks have been a popular choice among neural architectures for machine reading comprehension and question answering. While recent work revealed that memory networks can'…
Rule induction for global explanation of trained models
Madhumita Sushil, Simon Šuster, Walter Daelemans
Understanding the behavior of a trained network and finding explanations for its outputs is important for improving the network's performance and generalization ability, and for en…
Patient representation learning and interpretable evaluation using clinical notes
Madhumita Sushil, Simon Šuster, Kim Luyckx +1
We have three contributions in this work: 1. We explore the utility of a stacked denoising autoencoder and a paragraph vector model to learn task-independent dense patient represen…
CliCR: A Dataset of Clinical Case Reports for Machine Reading Comprehension
Simon Šuster, Walter Daelemans
We present a new dataset for machine comprehension in the medical domain. Our dataset uses clinical case reports with around 100,000 gap-filling queries about these cases. We apply…