6/12(Wed.)
Prof. Bing Liu (University of Illinois at Chicago, USA)
“Lifelong Learning for Sentiment Analysis” Download
Short bio
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 (AI) from the University of Edinburgh. Before joining UIC, he was a faculty member at the School of Computing, National University of Singapore (NUS). His research interests include sentiment analysis, lifelong learning, natural language processing (NLP), data mining, machine learning, and AI. He has published extensively in top conferences and journals. Two of his papers have 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 also 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 served as the Chair of ACM SIGKDD from 2013- 2017, as program chair of many leading data mining conferences, including KDD, ICDM, CIKM, WSDM, SDM, and PAKDD, as associate editor of 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. He is a Fellow of the ACM, AAAI, and IEEE.
The classic machine learning (ML) paradigm works in isolation and makes the closed-world assumption. Given a dataset, a ML algorithm is executed on the data to produce a model. The algorithm does not consider any other information in model training and the trained model cannot handle any unexpected situations in testing or applications. Although this paradigm has been very successful, it requires a large amount of training data, and is only suitable for well-defined, static and narrow domains. In contrast, we humans learn quite differently. We always learn with the help of our prior knowledge. We learn continuously, accumulate the knowledge learned in the past, and use it to help future learning and problem solving. When faced with an unfamiliar situation in an open environment, we adapt our knowledge to deal with the situation and learn from it. Lifelong learning aims to achieve this capability. In this talk, I will give an introduction to lifelong learning and discuss some of its applications in sentiment analysis and beyond.