Relation extraction is a subtask of information extraction where semantic relationships are
extracted from natural language text and then classified. In essence, it allows us to acquire …

Labeling training data is increasingly the largest bottleneck in deploying machine learning
systems. We present Snorkel, a first-of-its-kind system that enables users to train state-of-the …

Labeling training data is increasingly the largest bottleneck in deploying machine learning
systems. We present Snorkel, a first-of-its-kind system that enables users to train state-of-the …

Large labeled training sets are the critical building blocks of supervised learning methods
and are key enablers of deep learning techniques. For some applications, creating labeled …

As deep learning models are applied to increasingly diverse problems, a key bottleneck is
gathering enough high-quality training labels tailored to each task. Users therefore turn to …

In this survey, we provide an overview of the literature on knowledge graphs (KGs) in the
context of information retrieval (IR). Modern IR systems can benefit from information …

Training accurate classifiers requires many labels, but each label provides only limited
information (one bit for binary classification). In this work, we propose BabbleLabble, a …

Curating labeled training data has become the primary bottleneck in machine learning.
Recent frameworks address this bottleneck with generative models to synthesize labels at …

Automated seizure detection from electroencephalography (EEG) would improve the quality
of patient care while reducing medical costs, but achieving reliably high performance across …

Visual knowledge bases such as Visual Genome power numerous applications in computer
vision, including visual question answering and captioning, but suffer from sparse …