Document Classification: Features and Models

This lecture covers the concept of document classification, where a classifier is constructed to assign labels to unlabeled documents based on training data. It explains the use of document vectors, words, phrases, and metadata as features in classification models like k-Nearest-Neighbors, Naïve Bayes, and word embeddings. The challenges of dealing with high dimensionality and the implementation of classification models are also discussed, along with self-attention mechanisms and transformer models.