Events Calendar

CAIS Seminar: Dr. Xiang Ren (USC) - Learning Text Structures with Weak Supervision

Speaker: Dr. Xiang Ren, USC

Talk Title: Learning Text Structures with Weak Supervision

Series: USC Center for Artificial Intelligence in Society (CAIS) Seminar Series

Abstract: The real-world data, though massive, are hard for machines to resolve as they are largely unstructured and in the form of natural-language text. One of the grand challenges is to turn such massive corpora into machine-actionable structures. Yet, most existing systems have heavy reliance on human effort in the process of structuring various corpora, slowing down the development of downstream applications. In this talk, I will introduce an effort-light framework that extracts structured facts from massive corpora without task-specific human labeling effort. I will briefly introduce several interesting learning frameworks for structure extraction, and will share some directions towards mining corpus-specific structured networks for knowledge discovery.

This lecture satisfies requirements for CSCI 591: Research Colloquium


Biography: Xiang Ren is an Assistant Professor in the Department of Computer Science at USC affiliated with USC ISI. Xiang was a visiting researcher at Stanford University and received his PhD in CS at UIUC. He is interested in computational methods and systems that extract machine-actionable knowledge from massive unstructured text data, and is particularly excited about problems in the space of modeling sequence and graph data under weak supervision (learning with partial/noisy labels, and semi-supervised learning) and indirect supervision (multi-task learning, transfer learning, and reinforcement learning).

Location: Mark Taper Hall Of Humanities (THH) - 301

Audiences: Everyone Is Invited

Contact: Computer Science Department