Bio
Heng Ji is a professor at Computer Science Department, and an affiliated faculty member at Electrical and Computer Engineering Department of University of Illinois at Urbana-Champaign. She is also an Amazon Scholar. She received her B.A. and M. A. in Computational Linguistics from Tsinghua University, and her M.S. and Ph.D. in Computer Science from New York University. Her research interests focus on Natural Language Processing, especially on Multimedia Multilingual Information Extraction, Knowledge Base Population and Knowledge-driven Generation. She was selected as “Young Scientist” and a member of the Global Future Council on the Future of Computing by the World Economic Forum in 2016 and 2017. The awards she received include “AI’s 10 to Watch” Award by IEEE Intelligent Systems in 2013, NSF CAREER award in 2009, Google Research Award in 2009 and 2014, IBM Watson Faculty Award in 2012 and 2014 and Bosch Research Award in 2014-2018, and ACL2020 Best Demo Paper Award. She was invited by the Secretary of the U.S. Air Force and AFRL to join Air Force Data Analytics Expert Panel to inform the Air Force Strategy 2030. She is the lead of many multi-institution projects and tasks, including the U.S. ARL projects on information fusion and knowledge networks construction, DARPA DEFT Tinker Bell team and DARPA KAIROS RESIN team. She has coordinated the NIST TAC Knowledge Base Population task since 2010. She has served as the Program Committee Co-Chair of many conferences including NAACL-HLT2018. She is elected as the North American Chapter of the Association for Computational Linguistics (NAACL) secretary 2020-2021. Her research has been widely supported by the U.S. government agencies (DARPA, ARL, IARPA, NSF, AFRL, DHS) and industry (Amazon, Google, Bosch, IBM, Disney).
Keynote: Can We Predict the Future? Schema-Guided Complex Event Extraction and Prediction
Understanding events and communicating about them are fundamental human activities. Our minds represent events at various levels of granularity and abstraction, which allows us to quickly access and reason about old and new scenarios. Progress in natural language understanding and computer vision has helped automate some parts of event understanding but the current, first-generation, automated event understanding is overly simplistic since it is local, sequential and flat. Real events are hierarchical and probabilistic. Understanding them requires knowledge in the form of a repository of abstracted event schemas (complex event templates), understanding the progress of time, using background knowledge, and performing global inference and event prediction. In this talk I will present a new research direction on schema-guided complex event extraction and prediction. Event schemas encode knowledge of stereotypical structures of events and their connections. Event schemas can guide our understanding and ability to make predictions with respect to what might happen next. As events unfold, schemas are crucial to act as a scaffolding. Previous work on event schema induction either focus on atomic events or linear temporal event sequences, ignoring the interplay between events via arguments and argument relations. I will introduce a new concept of Temporal Complex Event Schema: a graph-based schema representation that encompasses events, arguments, temporal connections and argument relations. I will describe a neural graph generation approach to induce complex event schemas automatically, and then show how to apply the induced schema library to enhance end-to-end joint entity, relation and event extraction. We then apply our framework to instantiate high-level event schemas, and effectively predict types of events and participants that are likely to happen next, or some causal or conditional event types that may have happened in the past.