Abstract: Understanding events and communicating about them are fundamental human activities. However, it's much more difficulty to populate event-related knowledge compared to entity-related knowledge. For example, most people in the United States will be able to answer the question "Who is President Barack Obama’s wife?", but very few people can give a complete answer to "Who died in September 11 attacks?". We propose a new research direction on event-centric knowledge base construction from multimedia multilingual sources. 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. Our approach to second-generation event understanding builds on an incidental supervision approach to inducing an event schema repository that is probabilistic, hierarchically organized and semantically coherent. Low level primitive components of event schemas are abundant, and can be part of multiple, sparsely occurring, higher-level schemas. Consequently, we combine bottom-up data driven approaches across multiple modalities with top-down consolidation of information extracted from a smaller number of encyclopedic resources. This facilitates inducing higher-level event and time representations analysts can interact with, and allow them to guide further reasoning and extract events by constructing a novel structured cross-media common semantic space. When complex events unfold in an emergent and dynamic manner, the multimedia multilingual digital data from traditional news media and social media often convey conflicting information. To understand the many facets of such complex, dynamic situations, we have also developed cross-media cross-document event coreference resolution and event-event relation tracking methods for event-centric knowledge population.

Bio: Heng Ji is a professor at Computer Science Department of University of Illinois at Urbana-Champaign. 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 Information Extraction and Knowledge Base Population. She is 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 and NSF CAREER award in 2009. 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.