Text Handling: Matrix, Documents, Topics

This lecture covers the handling of text data, focusing on matrices, documents, and topics. It delves into tasks like document retrieval, classification, sentiment analysis, and topic detection using TF-IDF matrices. The instructor explains the challenges of high model capacity in document classification and introduces regularization techniques. The lecture also explores matrix factorization for topic detection and discusses the use of latent semantic analysis. Additionally, it touches on word vectors, contextualized word vectors, and advanced models like BERT for text representation. The presentation concludes with a discussion on natural language processing pipelines and the importance of joint learning in NLP tasks.