Neural Nets for NLP 2017 - Unsupervised Learning of Structure
Explore unsupervised learning of structure in natural language processing through this comprehensive lecture from CMU's Neural Networks for NLP course. Delve into the differences between learning features and learning structure, various unsupervised learning methods, and key design decisions for unsupervised models. Examine real-world examples of unsupervised learning, including hidden Markov models, CRF autoencoders, and dependency induction with neural networks. Gain insights into advanced topics such as learning with reinforcement learning, phrase structure vs. dependency structure, and learning language-level features. Access accompanying slides and related course materials to enhance your understanding of this complex subject in computational linguistics and machine learning.