The Berkeley NLP Group

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Teaching

Course Materials

• Interactive Assignments for Teaching Structured Neural NLP were developed for our graduate NLP course. They teach structured prediction using a combination of modern neural architectures and classic inference algorithms (in PyTorch and CoLab).
• The Pac-Man projects are a set of class projects that teach foundational AI concepts, such as informed state-space search, probabilistic inference, and reinforcement learning.

Tutorials

• Variational Inference for Structured NLP Models, David Burkett & Dan Klein, Presented at NAACL 2012 and ACL 2013. [pdf slides] [pdf slides (6pp)] [pdf handout]
• Structured Bayesian Nonparametric Models with Variational Inference, Percy Liang & Dan Klein, Presented at ACL 2007. [pdf]
• Introduction to Classification: Likelihoods, Margins, Features, and Kernels, Dan Klein, Presented at NAACL 2007. [pdf]
• Max-Margin Methods for NLP: Estimation, Structure, and Applications, Dan Klein & Ben Taskar, Presented at ACL 2005. [pdf]
• Maxent Models, Conditional Estimation, and Optimization, without the Magic, Dan Klein & Chris Manning, Presented at NAACL 2003 and ACL 2003. [pdf slides] [pdf handouts]
• Lagrange Multipliers without Permanent Scarring, Dan Klein. Permanently in rough draft form, it seems! [pdf-draft]