Learning Commonsense Event Schemas from Unlabeled Text

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• Nate Chambers, US Naval Academy
• Friday 20 January 2017, 12:00-13:00
• FW26, Computer Laboratory.

If you have a question about this talk, please contact Kris Cao.

The early years of research in natural language understanding focused on big picture representations like scripts and frames to drive language understanding. These formalisms motivated a rich line of research, but they suffered from brittle hand-coded structure. Most recently, several lines of research have focused on new computational methods to learn script-like knowledge. Event schema induction, for instance, is the task of learning high-level representations of complex events (e.g., a bombing) and their entity roles (e.g., perpetrator and victim) from unlabeled text. As is usual, many methods have been proposed, and different event representations and learning approaches have been applied. This talk will motivate why such schema learning is important to NLU , and present a particular generative model that learns schemas from unlabeled text without human supervision. It achieved state of the art performance on an information extraction domain. Further, the talk will briefly summarize the latest work in the area, and describe a new dataset to motivate future research in event schema learning.

This talk is part of the NLIP Seminar Series series.

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