CMU Neural Nets for NLP - Distributional Semantics and Word Vectors

CMU Neural Nets for NLP - Distributional Semantics and Word Vectors

Explore distributional semantics and word vectors in this comprehensive lecture from CMU's Neural Networks for NLP course. Delve into describing words by their context, counting and predicting techniques, skip-grams and CBOW models, and methods for evaluating and visualizing word vectors. Learn about advanced word vector techniques, including contextualization, WordNet, and various embedding models like CBOW and GloVe. Examine different types of context, evaluation methods for embeddings, and their practical applications in NLP systems. Discuss the limitations of embeddings and explore sub-word embedding techniques. Gain valuable insights into the foundations of word representations in natural language processing.