Faculty Summit 2016 – Machine Reading for Science and Society


July 14, 2016


Chris Quirk, Oren Etzioni, Chris M Re, Hoifung Poon


Microsoft, Allen Institute for Artificial Intelligence, Stanford University, Microsoft


Machine reading automates knowledge extraction from text. Traditional machine learning methods are hindered by the scarcity of annotated examples, which motivates the development of modern approaches that leverage “free lunches” such as existing ontologies and databases for indirect supervision. This has enabled the scope of machine reading to expand significantly from its traditional newswire focus. The ensuing impact to science and society is just beginning to manifest. In this session, we will give an overview of modern machine reading approaches and highlight some example applications such as cancer precision medicine, fighting human trafficking, and education.