Berkeley NLP Group

Resources

Courses

EECS 283A: Natural Language Processing (Fall 2026)
CS 294-288: Data-Centric LLMs (Fall 2026)
CS 288: Advanced Natural Language Processing (Spring 2026)
CS 294-258: Computational Models of Language Use and Interaction (Spring 2026)
CS 294-258: Language Agents in Interaction (Spring 2024)

Software

The Berkeley Crossword Solver combines state-of-the-art QA with constraint solving.
The Berkeley Neural Parser infers syntactic annotations using neural networks and self-attention. We have an online demo!
The Berkeley Parser Analyser performs error analysis on parse trees.
The Berkeley Aligner aligns the words in multilingual parallel texts.
Ocular is a state-of-the-art historical document recognition system.

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]