82 citations · 121 across the 9 of their papers we have counts for
11 papers
This Patient Looks Like That Patient: Prototypical Networks for Interpretable Diagnosis Prediction from Clinical Text
Betty van Aken, Jens-Michalis Papaioannou, Marcel G. Naik +4
The use of deep neural models for diagnosis prediction from clinical text has shown promising results. However, in clinical practice such models must not only be accurate, but prov…
Cross-Lingual Knowledge Transfer for Clinical Phenotyping
Jens-Michalis Papaioannou, Paul Grundmann, Betty van Aken +5
Clinical phenotyping enables the automatic extraction of clinical conditions from patient records, which can be beneficial to doctors and clinics worldwide. However, current state-…
Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration
Betty van Aken, Jens-Michalis Papaioannou, Manuel Mayrdorfer +3
Outcome prediction from clinical text can prevent doctors from overlooking possible risks and help hospitals to plan capacities. We simulate patients at admission time, when decisi…
VisBERT: Hidden-State Visualizations for Transformers
Betty van Aken, Benjamin Winter, Alexander Löser +1
Explainability and interpretability are two important concepts, the absence of which can and should impede the application of well-performing neural networks to real-world problems…
Learning Contextualized Document Representations for Healthcare Answer Retrieval
Sebastian Arnold, Betty van Aken, Paul Grundmann +2
We present Contextual Discourse Vectors (CDV), a distributed document representation for efficient answer retrieval from long healthcare documents. Our approach is based on structu…
How Does BERT Answer Questions? A Layer-Wise Analysis of Transformer Representations
Betty van Aken, Benjamin Winter, Alexander Löser +1
Bidirectional Encoder Representations from Transformers (BERT) reach state-of-the-art results in a variety of Natural Language Processing tasks. However, understanding of their int…
SECTOR: A Neural Model for Coherent Topic Segmentation and Classification
Sebastian Arnold, Rudolf Schneider, Philippe Cudré-Mauroux +2
When searching for information, a human reader first glances over a document, spots relevant sections and then focuses on a few sentences for resolving her intention. However, the…
Crowd-Labeling Fashion Reviews with Quality Control
Iurii Chernushenko, Felix A. Gers, Alexander Löser +1
We present a new methodology for high-quality labeling in the fashion domain with crowd workers instead of experts. We focus on the Aspect-Based Sentiment Analysis task. Our method…
IDEL: In-Database Entity Linking with Neural Embeddings
Torsten Kilias, Alexander Löser, Felix A. Gers +3
We present a novel architecture, In-Database Entity Linking (IDEL), in which we integrate the analytics-optimized RDBMS MonetDB with neural text mining abilities. Our system design…
Analysing Errors of Open Information Extraction Systems
Rudolf Schneider, Tom Oberhauser, Tobias Klatt +2
We report results on benchmarking Open Information Extraction (OIE) systems using RelVis, a toolkit for benchmarking Open Information Extraction systems. Our comprehensive benchmar…
Robust Named Entity Recognition in Idiosyncratic Domains
Sebastian Arnold, Felix A. Gers, Torsten Kilias +1
Named entity recognition often fails in idiosyncratic domains. That causes a problem for depending tasks, such as entity linking and relation extraction. We propose a generic and r…