Joint Models for Social, Behavioral and Textual Information

Abstract:

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.

Bio:

Dan Goldwasser is an assistant professor in the Department of Computer Science at Purdue University. His research interest include natural language processing, social media and political discourse analysis and machine learning. 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.