Events Calendar

CS Colloquium: Vinodkumar Prabhakaran (Stanford University) - NLP for Social Good: Inferring Social Context from Language

Speaker: Vinodkumar Prabhakaran, Stanford University

Talk Title: NLP for Social Good: Inferring Social Context from Language

Series: CS Colloquium

Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium.

The vast quantities of language data online and offline offer tremendous opportunities to study society through language. In this talk, I show how natural language processing techniques can be expanded from understanding the meanings of words and sentences, to inferring the underlying social structures and processes they reflect and identifying crucial shortcomings in them. I apply these techniques to computationally detect two ways in which the social context affects the use of language: social relations affecting how people interact with one another, and social constructs shaping how institutions interact with communities. In the first part, I show how to computationally detect manifestations of social power in workplace interactions between individuals -” providing means for organizations to detect incivility at workplace. In the second part, I show how to computationally investigate the ways race shapes the interactions between the police and the communities they serve -” providing means for departments to address and monitor racial disparities in policing. My research looks beyond words and phrases, and introduce ways to infer richer rhetorical and dialog information like conversational structure and respect that reflect the social context, demonstrating the importance of deeper language processing for the computational social sciences.

Biography: Vinodkumar Prabhakaran is a postdoctoral fellow in the computer science department at Stanford University. His research falls in the inter-disciplinary field of computational sociolinguistics, in which he builds and uses computational tools to analyze linguistic patterns that reveal the underlying social contexts in which language is used. He received his PhD in Computer Science from Columbia University in 2015. In his doctoral thesis, he studied how machine learning and natural language processing techniques can help detect the underlying social power structures that guide social interactions. As part of his research, he has also made significant contributions to core NLP problems such as extracting information from text, as well as modeling structures of dialog and discourse.

Host: CS Department

Location: Ronald Tutor Hall of Engineering (RTH) - 217

Audiences: Everyone Is Invited

Contact: Assistant to CS chair