Explore a comprehensive lecture on unsupervised and semi-supervised learning of structure in neural networks for natural language processing. Delve into the distinctions between learning features and learning structure, examine various semi-supervised and unsupervised learning methods, and understand key design decisions for unsupervised models. Gain insights into practical examples of unsupervised learning, including cross-lingual applications and the challenges of learning latent structure. Learn about advanced concepts such as normalizing flow, gated convolution, and reinforcement learning approaches in the context of NLP. Discover how to leverage weak supervision and navigate the complexities of soft vs. hard tree structures in neural network architectures.