Understanding natural language communication often requires context, such as the speakers’ backgrounds and social conventions, however, when it comes to computationally modeling these interactions, we typically ignore their broader context and analyze the text in isolation. In this talk, I will review on-going work demonstrating the importance of holistically modeling behavioral, social, and textual information. I will focus on several NLP problems, including political discourse analysis on Twitter and partisan news detection, and discuss how jointly modeling text and social behavior can help reduce the supervision effort and provide a better representation for language understanding tasks.
Dan Goldwasser is an Associate Professor at the Department of Computer Science at Purdue University. He is broadly interested in connecting natural language with real world scenarios and using them to guide natural language understanding. His current interests focus on grounding political discourse to support understanding real-world scenarios, using neuro-symbolic representations. Dan Completed his PhD in Computer Science at the University of Illinois at Urbana-Champaign and was a postdoctoral researcher at the University of Maryland. He has received research support from the NSF, including a recent CAREER award, DARPA and Google.
Host: Muhao Chen POC: Maura Covaci
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