时间：2024年1月17日（周三） 10:00-11:30

Time: 10:00-11:30, Wednesday, January 17, 2024

线上：ZOOM ID 881 1116 7126

Venue: ZOOM Meeting ID: 881 1116 7126

主持人: 西湖大学工学院讲席教授 金耀初

Host: Yaochu Jin, Chair Professor of Artificial Intelligence, School of Engineering

报告语言：英文

Language: English

主讲嘉宾/Speaker：

Prof. Jiawei Han

Michael Aiken Chair Professor,
Department of Computer Science,

University of Illinois at Urbana-Champaign

主讲人简介/Biography:

Jiawei Han is Michael Aiken Chair Professor in the Department of Computer Science, University of Illinois at Urbana-Champaign.  He received ACM SIGKDD Innovation Award (2004), IEEE Computer Society Technical Achievement Award (2005), IEEE Computer Society W. Wallace McDowell Award (2009), Japan's Funai Achievement Award (2018), and was elevated to Fellow of Royal Society of Canada (2022). He is Fellow of ACM and Fellow of IEEE and served as the Director of Information Network Academic Research Center (INARC) (2009-2016) supported by the Network Science-Collaborative Technology Alliance (NS-CTA) program of U.S. Army Research Lab and co-Director of KnowEnG, a Center of Excellence in Big Data Computing (2014-2019), funded by NIH Big Data to Knowledge (BD2K) Initiative. Currently, he is serving on the executive committees of two NSF funded research centers: MMLI (Molecular Make Research Institute)—one of NSF funded national AI centers since 2020 and I-Guide—The National Science Foundation (NSF) Institute for Geospatial Understanding through an Integrative Discovery Environment (I-GUIDE) since 2021.

讲座摘要/Abstract:

The emergence of various language models greatly impacts mining structures from text; on the other hand, structuring massive text will help construct quality language models as well. Although language models may sometimes generate hallucinated contents, a proper usage of them will endow us with the power to uncovering hidden semantics in natural languages. We examine our recent studies on using large language models to enhance the structuring of massive text, via a set of examples, including discriminative topic mining, text classification, taxonomy-guided information extraction, and construction of theme-specific knowledgebases. We show that equipped with LLMs, weakly supervised, annotation-free approach could be promising at transforming massive text into structured knowledge and benefiting many downstream applications. Moreover, we envision such structuring of knowledge will benefit the proper and innovative usage of large language models.

Contact:

Ms. Linh Chu, School of Engineering

chuyenlinh@westlake.edu.cn