Supervisionless Machine Learning with World Knowledge

Speaker:        Dr. Yangqiu Song
                University of Illinois at Urbana-Champaign

Title:          "Supervisionless Machine Learning with World Knowledge"

Date:           Monday, 22 June 2015

Time:           4:00pm - 5:00pm

Venue:          Lecture Theatre F (near lifts 25/26), HKUST

Abstract:

Machine learning algorithms have become pervasive in multiple domains and
have started to have impact in applications. Nonetheless, a key obstacle
in making learning protocol realistic in applications is the need to
supervise them, a costly process that often requires hiring domain
experts. However, while annotated data is difficult to get, we have
available large amounts of data from the Web. In this talk, I will
introduce learning paradigm which uses existing world knowledge to
"supervise" machine learning algorithms. By "world knowledge" we refer to
general-purpose knowledge collected from the Web, and that can be used to
extract both common sense knowledge and diverse domain specific knowledge
and thus help supervise machine learning algorithms. I will introduce the
supervisionless classification algorithm which requires no labeled data to
perform completely unsupervised text classification. In this case, the
world knowledge is embed to represent the text documents and the category
labels into the same semantic space. We can also perform better machine
learning and text data analytics by adapting general-purpose knowledge to
domain specific tasks.


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Biography:

Dr. Yangqiu Song is a post-doctoral researcher at the Cognitive
Computation Group at the University of Illinois at Urbana-Champaign.
Before that, he was a post-doctoral fellow at Hong Kong University of
Science and Technology and visiting researcher at Huawei Noah's Ark Lab,
Hong Kong (2012-2013), an associate researcher at Microsoft Research Asia
(2010-2012) and a staff researcher at IBM Research China (2009-2010)
respectively. He received his B.E. and Ph.D. degrees from Tsinghua
University, China, in July 2003 and January 2009, respectively. His
current research focuses on using machine learning and data mining to
extract and infer insightful knowledge from big data. The knowledge helps
users better enjoy their daily living and social activities, or helps data
scientists do better data analytics. He is particularly interested in
working on large scale learning algorithms, on natural language
understanding, text mining and visual analytics, and on knowledge
engineering for domain applications.