Explore transition-based dependency parsing in this comprehensive lecture from CMU's Neural Networks for NLP course. Delve into the fundamentals of transition-based parsing, including shift-reduce parsing with feed-forward networks and stack LSTMs. Learn about a simple alternative approach using linearized trees. Gain insights into various parsing techniques, feature extraction methods, and the importance of tree structures in natural language processing. Examine practical code examples and follow along with detailed slides to reinforce your understanding of these advanced NLP concepts.