Osprey: Weak Supervision of Imbalanced Extraction Problems Without Code
Proposing Osprey, a weak-supervision system suited for highly imbalanced data, built on top of the Snorkel framework.
Multi-Resolution Weak Supervision for Sequential Data
Proposing Dugong, the first framework to model multi-resolution weak supervision sources with complex correlations to assign probabilistic labels to training data.
Medical Device Surveillance With Electronic Health Records
Showcasing state-of-the-art deep learning methods that identify patient outcomes from clinical notes without requiring hand-labeled training data.
Learning Dependency Structures for Weak Supervision Models
This work focuses on a robust PCA-based algorithm for learning these dependency structures, establish improved theoretical recovery rates, and outperform existing methods on various real world tasks.
Interactive Programmatic Labeling for Weak Supervision
Demonstrating in synthetic and real-world experiments how two simple labeling function acquisition strategies outperform a random baseline.
Bootstrapping Conversational Agents with Weak Supervision
This paper presents a framework called search, label, and propagate (SLP) for bootstrapping intents from existing chat logs using weak supervision.
A Machine-Compiled Database of Genome-Wide Association Studies
Describing GWASkb, a machine-compiled knowledge base of genetic associations collected from the scientific literature using automated information extraction algorithms.
A Clinical Text Classification Paradigm Using Weak Supervision…
This work develops a rule-based NLP algorithm to automatically generate labels for the training data, and then use the pre-trained word embeddings as deep representation features for training machine learning models.