题目：Weakly Supervised Learning of Topic Model 报告人：Dr. Hang Li
Microsoft Research Asia 时间：5月27日， 星期三， 上午 10:30 - 11:30 地点：蒙民伟楼404会议室 摘要：
Topic Modeling is a powerful technology for data mining and search. In
this talk, I will first give a brief survey on Topic Modeling
techniques such as PLSI (Probabilistic Latent Semantic Indexing) and
LDA (Latent Dirichlet Allocation). Then, I will explain a novel Topic
Modeling technique which we have developed recently, called Weakly
Supervised Latent Dirichlet Allocation (WS-LDA). WS-LDA is unique in
that it leverages weak supervision from humans in learning. We have
applied WS-LDA to the task of named entity recognition from queries in
search. I will take it as example to explain the motivation, technical
details, and advantages of WS-LDA. Related papers will be presented
at KDD and SIGIR this year.
简历：
Hang Li is senior researcher and research manager in the Information
Retrieval and Mining Group at Microsoft Research Asia. He is also
adjunct professor at Peking University, Nanjing University, Xian
Jiaotong University, and Nankai University. His research areas include
natural language processing, information retrieval, statistical
machine learning, and data mining. He graduated from Kyoto University
and earned his PhD from the University of Tokyo. Hang has about 60
publications in international journals and conferences. He is
associate editor of ACM Transaction on Asian Language Information
Processing and is in editorial board of Journal for Computer and
Science Technology, Journal of Chinese Information Processing, etc.
His recent academic activities include program committee co-chair of
AIRS’08, poster and demo committee co-chair of SIGIR’08, program
committee area chair of PAKDD’08, etc. Hang has been working on
development of several products. These include NEC TopicScope,
Microsoft SQL Server 2005, Microsoft Office 2007, and Microsoft Live
Search 2008.
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