Physical Sciences and Mathematics Commons^™

Corrective Feedback And Persistent Learning For Information Extraction, Aron Culotta, Trausti Kristjansson, Andrew Mccallum, Paul Viola

Andrew McCallum

To successfully embed statistical machine learning models in real world applications, two post-deployment capabilities must be provided: (1) the ability to solicit user corrections and (2) the ability to update the model from these corrections. We refer to the former capability as corrective feedback and the latter as persistent learning. While these capabilities have a natural implementation for simple classification tasks such as spam filtering, we argue that a more careful design is required for structured classification tasks. One example of a structured classification task is information extraction, in which raw text is analyzed to automatically populate a database. In …