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AIC Seminar Series

What’s so Hard About Natural Language Understanding?

Alan RitterOhio State University[Home Page]

Notice:  Hosted by Dayne Freitag

Date:  Monday, May 14th 2018 at 11:00am

Location:  EK255 (SRI E building)  (Directions)


Much of human knowledge is readily available on the internet, however natural language is notoriously difficult for computers to interpret. In this talk, I present some recent advances in extracting structured knowledge from text with an eye toward realtime information in massive data streams found on social media. I argue we can not exclusively rely on traditional methods that learn from small, hand-annotated datasets if we hope to extract a broad range of relations and events from diverse text genres at scale. As an alternative to human labeling, I will describe an approach that reasons about latent variables to learn robust information extraction models from large, opportunistically gathered datasets. As a concrete example, I will present a new approach to resolving time expressions without relying on any hand-coded rules or manually annotated data. By leveraging a database of known events as distant supervision, in conjunction with large quantities of in-domain data, our approach can outperform off-the-shelf resolvers on noisy user-generated data.

   Bio for Alan Ritter

Alan Ritter is an assistant professor in computer science at Ohio State University. His research interests include natural language processing, social media analysis, and machine learning. Ritter completed his PhD at the University of Washington and was a postdoctoral fellow in the Machine Learning Department at Carnegie Mellon University. He has received an NDSEG fellowship, a best student paper award at IUI, an NSF CRII, and has served as an area chair for ACL, EMNLP, and NAACL.

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