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Data Mining Will Be Reborn with Large Language Models

ABSTRACT Data mining is a discipline that develops scalable and effective methods for knowledge discovery from massive data. However, most data mining studies have been focusing on mining structured, sequenced, […]

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Jan 22

January 22, 2024

4:00 pm - 4:00 pm

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  • LSRC D106

ABSTRACT
Data mining is a discipline that develops scalable and effective methods for knowledge discovery from massive data. However, most data mining studies have been focusing on mining structured, sequenced, and networked data although the real-world data is largely in highly unstructured, text form. With the emergence of deep learning, embedding, and large language models (LLMs), powerful new tools are being created for handling massive text data. In this talk, we examine some recent studies on applying large language models for natural language processing and text mining, including discriminative topic mining, text classification, and taxonomy-guided information extraction and construction of theme-specific knowledgebases. We show that equipped with LLMs, data mining-styled, weakly supervised approach could be promising at transforming massive text into structured knowledge and benefiting many downstream applications. We will also discuss what could be the future of data mining and text mining with the rapid development of large language models.
SPEAKER BIO
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.
TRIANGLE COMPUTER SCIENCE DISTINGUISHED LECTURER SERIES
The CS departments at Duke, NC State, and UNC-Chapel Hill joined forces to create the Triangle Computer Science Distinguished Lecturer Series. Read more: https://cs.unc.edu/news/tcsdls