Registration: The workshop is free to attend, but registration is required and space is limited.
Travel Awards: Travel awards are available. Please visit the workshop webpage for more information.
Deadline for Applications: May 31, 2017
Over 80% of the data available in the world today is currently unreadable by computers. These “dark data” are unstructured and include a wide range of invaluable information sources, from the text of scientific articles to the notes written by your doctor. Transforming these data into a form readable by machines is called knowledge base construction and is a vital process for unlocking the potential found in these resources.
Current approaches for automatically building knowledge bases require large, labeled datasets for training. These gold standard datasets are difficult to come by, particularly in biomedicine, limiting our ability to create new knowledge bases that can be analyzed.
Snorkel was created in response to this challenge. Developed in Christopher Re’s lab at Stanford University, Snorkel constructs knowledge bases from “dark data.” And unlike other approaches, which require precisely labeled data to train and build the models, Snorkel can work with just a set of user-input rules.
In this two-day workshop, you will learn to use the Snorkel platform through hands-on exercises and receive assistance in applying Snorkel to your own research questions.
This workshop is designed for individuals who are interested in applying state-of-the-art machine reading approaches to extracting information from the text and tables of documents. You do not need to know anything about machine reading or machine learning, but you should have some basic Python programming skills.
To learn more and apply, visit the workshop webpage.
Application deadline: May 31, 2017