Neural Nets for NLP - Transition-based Parsing
Learn about transition-based parsing in natural language processing through this comprehensive lecture from CMU's Neural Networks for NLP course. Explore the fundamentals of shift-reduce parsing with feed-forward networks, stack LSTMs, and transition-based models for phrase structure. Discover the concept of linearized trees as a simple alternative approach. Gain insights into various parsing techniques, including arc standard shift-reduce parsing, recursive neural networks, and methods for encoding parsing configurations. Understand the importance of tree structures in NLP and how to make classification decisions in parsing tasks. Delve into advanced topics such as non-linear functions and alternative transition methods to enhance your understanding of neural network-based parsing techniques.