Speaker: Marti A. Hearst, Professor in the School of Information and EECS, UC Berkeley
How we teach and learn is undergoing a revolution, due to changes in technology and connectivity.Education may be one of the best application areas for advanced NLP techniques, and NLP researchers have much to contribute to this problem, especially in the areas of learning to write, mastery learning, and peer learning. In this talk I consider what happens when we convert natural language processors into natural language coaches.
Marti Hearst is a Professor at UC Berkeley in the School of Information and EECS. She received her PhD in CS from UC Berkeley in 1994 and was a member of the research staff at Xerox PARC form 1994-1997. Her research is in computational linguistics, search user interfaces, information visualization, and improving learning at scale. Her NLP work includes automatic acquisition of hypernym relations ("Hearst Patterns"), TextTiling discourse segmentation, abbreviation recognition, and multiword semantic relations. She wrote the book "Search User Interfaces" (Cambridge) in 2009, co-founded the ACM Conference on Learning at Scale in 2014, and was named an ACM Fellow in 2013. She has received four student-initiated Excellence in Teaching Awards, including in 2014 and 2015.
Speaker: Jiawei Han, Abel Bliss Professor, Department of Computer Science, University of Illinois at Urbana-Champaign
The real-world data are unstructured but interconnected. The majority of such data is in the form of natural language text. One of the grand challenges is to turn such massive data into actionable knowledge. In this talk, we present our vision on how to turn massive unstructured, text-rich, but interconnected data into knowledge. We propose a data-to-network-to-knowledge (i.e., D2N2K) paradigm, which is to first turn data into relatively structured heterogeneous information networks, and then mine such text-rich and structure-rich heterogeneous networks to generate useful knowledge. We show why such a paradigm represents a promising direction and present some recent progress on the development of effective methods for construction and mining of structured heterogeneous information networks from text data.
Jiawei Han is Abel Bliss Professor in the Department of Computer Science, University of Illinois at Urbana-Champaign. He has been researching into data mining, information network analysis, database systems, and data warehousing, with over 600 journal and conference publications. He has chaired or served on many program committees of international conferences, including PC co-chair for KDD, SDM, and ICDM conferences, and Americas Coordinator for VLDB conferences. He also served as the founding Editor-In-Chief of ACM Transactions on Knowledge Discovery from Data and is serving as the Director of Information Network Academic Research Center supported by U.S. Army Research Lab, and Director of KnowEnG, an NIH funded Center of Excellence in Big Data Computing. He is a Fellow of ACM and Fellow of IEEE, and received 2004 ACM SIGKDD Innovations Award, 2005 IEEE Computer Society Technical Achievement Award, 2009 IEEE Computer Society Wallace McDowell Award, and 2011 Daniel C. Drucker Eminent Faculty Award at UIUC. His co-authored book "Data Mining: Concepts and Techniques" has been adopted as a textbook popularly worldwide.