Text Representation Learning: Word Embeddings

This lecture covers the fundamental concept of text representation learning, focusing on word embeddings. It explains the process of learning numerical representations for words using techniques like matrix factorization. The lecture delves into the co-occurrence matrix, GloVe model, and learning word representations through matrix factorization. It also discusses word analogies and the training methods involved in learning word embeddings. Additionally, it explores the use of large models like transformers in predicting the next word in a text. The lecture concludes with pointers to resources on word2vec, GloVe, FastText, and sent2vec.