7 December 2018 - Bing Liu: Seminar

TITLE:

From Sentiment Analysis to Lifelong Machine Learning

ABSTRACT:

Sentiment analysis (SA) or opinion mining has been an active research area in natural language processing (NLP) due to a wide range of applications. In this talk, I will first give a brief introduction to SA, and then move on to our recent work on lifelong (machine) learning (LL), which was motivated by my experiences in SA. LL aims to imitate human learning. It learns a series of tasks continuously, accumulates the knowledge learned from previous tasks, and leverages the knowledge in learning new tasks. However, the classic machine learning (ML) paradigm learns in isolation: given a training dataset, it runs a ML algorithm on the dataset to produce a model. It makes no attempt to retain the learned knowledge and use it in subsequent learning. Humans never learn in isolation. This classic ML paradigm has some major shortcomings, which makes it unsuitable for many practical applications such as chatbots and self-driving cars which need to face the real dynamic and open world. They have to learn continuously in their environments in order to function well. In this talk, I will introduce this emerging LL paradigm and discuss some of our recent work, including applications in sentiment analysis.

BIOGRAPHY:

Bing Liu is a distinguished professor of Computer Science at the University of Illinois at Chicago (UIC). He received his Ph.D. in Artificial Intelligence from University of Edinburgh. Before joining UIC, he was a faculty member at the School of Computing, National University of Singapore. His research interests include sentiment analysis, lifelong learning, natural language processing (NLP), data mining, and machine learning. He has published extensively in top conferences and journals. Two of his papers received Test-of-Time awards from SIGKDD (ACM Special Interest Group on Knowledge Discovery and Data Mining). He is also a recipient of ACM SIGKDD Innovation Award, the most prestigious technical award from SIGKDD. He has authored four books: two on sentiment analysis, one on lifelong learning, and one on Web mining. Some of his work has been widely reported in the international press, including a front-page article in the New York Times. On professional services, he has served as program chair of many leading data mining conferences, including KDD, ICDM, CIKM, WSDM, SDM, and PAKDD, as associate editor of many leading journals such as TKDE, TWEB, DMKD and TKDD, and as area chair or senior PC member of numerous NLP, AI, Web, and data mining conferences. Additionally, he served as the Chair of ACM SIGKDD from 2013-2017. He is a Fellow of the ACM, AAAI, and IEEE.

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