Constrained Conditional Models: Integer Linear Programming Formulations for Language Understanding

Explore Constrained Conditional Models (CCMs) and their applications in Natural Language Understanding through this informative lecture by Professor Dan Roth. Delve into the Integer Linear Programming formulation that enhances probabilistic models with declarative constraints for tasks like semantic role labeling, syntactic parsing, information extraction, name transliteration, and textual entailment. Examine recent research findings on inference issues, learning algorithms for training global models, and the interplay between learning and inference. Gain insights from Professor Roth, a distinguished ACM and AAAI Fellow known for his contributions to Machine Learning and Natural Language Processing, as he shares his expertise in this hour-long presentation from the Center for Language & Speech Processing at Johns Hopkins University.

Constrained Conditional Models: Integer Linear Programming Formulations for Language Understanding