activity
20172022
most citedLearning Contextualized Document Representations for Healthcare Answer Retrieval

12 citations · 22 across the 5 of their papers we have counts for

collaborators

8 papers

cs.CL20212 cited

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…

cs.CL20202 cited

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…

cs.CL202012 cited

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…

cs.CL2019

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…

cs.CL2019

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…

cs.CL2018

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…