题目：Knowledge is Power in Data-Driven NLP 报告人：Ye-Yi Wang 博士，Microsoft 时间：11月11日(星期三) 下午2：00 地点：蒙民伟楼109 摘要：
The theories of language acquisition are often categorized into two camps –
the rational approach states that most of the human language faculty is innate
, while the empirical approach stresses that the languages are mostly acquired
through external experience. Reflected in the field of NLP, we have seen the
knowledge-based approach and the data-driven approach. This simple
categorization ignores the facts that the rationalists never deny the effect
of external experience and the empiricists also admit some basic hardwired
rules in human brain. In case of NLP, it also makes sense to integrate the
knowledge-based and the data-driven approaches. For over a decade, we have been working on different aspects of data-driven NL
P, including statistical MT, language modeling, spoken language understanding,
and information extraction. A common lesson learned from all the project is
the power of knowledge – it helped either improve the performance or reduce
the requirement of training data significantly. In this talk I will review
some of the projects and show in details how knowledge can be learned and
integrated in data-driven NLP systems. 简历：
Ye-Yi Wang received a B.S. and a M.S. degree in Computer Science from Shanghai
Jiao Tong University, a Master’s degree in Computational Linguistics from
Carnegie Mellon University and a Ph.D. degree in Human Language Technology
from Carnegie Mellon University. Dr. Wang joined Microsoft Research (Redmond,
WA) in 1998. His research interests include user intent understanding, spoken
dialog systems, natural language processing, information retrieval, language
modeling, statistical machine translation and machine learning. He served in
the editorial board of the Chinese Contemporary Linguistic Theory Series. He
is a co-author of Introduction to Computational Linguistics (China Social
Sciences Publishing House, 1997), and he has published over 50 journal and
conference papers. Dr. Wang is a member of ACL and ACM, and a senior member of
IEEE.
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