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Found 10 papers, 8 papers with code
This Patient Looks Like That Patient: Prototypical Networks for Interpretable Diagnosis Prediction from Clinical Text
The use of deep neural models for diagnosis prediction from clinical text has shown promising results.
Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration
Outcome prediction from clinical text can prevent doctors from overlooking possible risks and help hospitals to plan capacities.
Ranked #1 on
Length-of-Stay prediction
on Clinical Admission Notes from MIMIC-III
(using extra training data)
VisBERT: Hidden-State Visualizations for Transformers
At the same time, they are difficult to incorporate into the large, black-box models that achieve state-of-the-art results in a multitude of NLP tasks.
Learning Contextualized Document Representations for Healthcare Answer Retrieval
Our model leverages a dual encoder architecture with hierarchical LSTM layers and multi-task training to encode the position of clinical entities and aspects alongside the document discourse.
How Does BERT Answer Questions? A Layer-Wise Analysis of Transformer Representations
In order to better understand BERT and other Transformer-based models, we present a layer-wise analysis of BERT's hidden states.
SECTOR: A Neural Model for Coherent Topic Segmentation and Classification
From our extensive evaluation of 20 architectures, we report a highest score of 71. 6% F1 for the segmentation and classification of 30 topics from the English city domain, scored by our SECTOR LSTM model with bloom filter embeddings and bidirectional segmentation.
Crowd-Labeling Fashion Reviews with Quality Control
We present a new methodology for high-quality labeling in the fashion domain with crowd workers instead of experts.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
IDEL: In-Database Entity Linking with Neural Embeddings
We present a novel architecture, In-Database Entity Linking (IDEL), in which we integrate the analytics-optimized RDBMS MonetDB with neural text mining abilities.
Analysing Errors of Open Information Extraction Systems
We report results on benchmarking Open Information Extraction (OIE) systems using RelVis, a toolkit for benchmarking Open Information Extraction systems.
Robust Named Entity Recognition in Idiosyncratic Domains
We propose a generic and robust approach for high-recall named entity recognition.
