Structured Approaches to Natural Language Understanding

Abstract

Despite recent advancements in Natural Language Processing, computers today cannot understand text in the ways that humans can. My research aims at creating computational methods that not only read but also understand text. To accomplish this, I develop machine-learning methods that incorporate linguistic cues as well as the context in which they appear to understand language. In this talk, I will discuss two specific applications of language understanding that focus on comprehension of narratives: (i) Choosing correct endings to stories, and (ii) Automatically generating narratives. I will also discuss my ongoing and future work on applications of language understanding in domains like education, digital humanities and mental health care.

Bio

Snigdha Chaturvedi is an Assistant Professor in the department of Computer Science and Engineering at University of California, Santa Cruz. She specializes in the field of Natural Language Processing with an emphasis on developing methods for natural language understanding. Her research has been recognized with the IBM Ph.D. Fellowship (twice), a best paper award at NAACL, and first prize at ACM student research competition held at Grace Hopper Conference. Previously, she was a postdoctoral fellow at University of Illinois, Urbana Champaign, and University of Pennsylvania working with Professor Dan Roth. She earned her Ph.D. in Computer Science at University of Maryland, College Park in 2016 (advisor: Dr. Hal Daume III) and Bachelors of Technology from Indian Institute of Technology, Kanpur in 2009. She was also a Blue Scholar at IBM Research, India from 2009 to 2011.