[Intelligence Seminar] TOMORROW: David Blei, Wean 5409, 3:30 - "Supervised and Relational Topic Models"
Noah A Smith
nasmith at cs.cmu.edu
Mon Mar 16 09:33:29 EDT 2009
March 17, 2009
For meetings, contact Noah Smith (nasmith at cs.cmu.edu).
Supervised and relational topic models
A surge of recent research in machine learning and statistics has
developed new techniques for finding patterns of words in document
collections using hierarchical probabilistic models. These models are
called "topic models" because the discovered word patterns often
reflect the underlying topics that permeate the documents. Topic
models also naturally apply to data such as images and biological
In this talk I will review the basics of topic modeling, and discuss
some recent extensions: supervised topic modeling and relational topic
modeling. Supervised topic models allow us to use topics in a setting
where we seek both exploratory and predictive power. Relational topic
models---which are built on supervised topic models---consider
documents interconnected in a graph. These models can be used to
summarize a network of documents, predict links between them, and
predict words within them.
Joint work with Jonathan Chang and Jon McAuliffe.
David Blei is an assistant professor in the Computer Science
department at Princeton University. He received his Ph.D. in 2004
from U.C. Berkeley and was a postdoctoral researcher in the Department
of Machine Learning at Carnegie Mellon University. His research
interests include graphical models, approximate posterior inference,
and nonparametric Bayesian statistics. He focuses on applications to
information retrieval and natural language processing.
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