Neural Networks for NLP

This lecture provides a broad overview of modern Neural Network approaches to Natural Language Processing (NLP). It covers the evolution of corpus-based linguistics, the importance of word embeddings, and the use of Neural Networks for NLP tasks. The lecture discusses key concepts such as learning word representations, the use of Neural Networks for transforming vectors into outputs, and the application of models like word2vec, Glove, and fastText. Additionally, it explores the advantages and drawbacks of Neural Networks in NLP, including the use of Multi-Layer Perceptrons (MLP) and the learning procedures involved. The lecture concludes with insights into the future of NLP, including Transfer Learning techniques like ULMFIT, ELMO, and BERT.