MLG 019 Natural Language Processing 2

This podcast episode explores the key concepts and algorithms in natural language processing (NLP). It introduces classical NLP algorithms, including shallow learning and deep learning approaches, highlighting the importance of understanding shallow algorithms despite the prevalence of deep learning. The episode covers topics such as edit distance, n-grams, joint probability, Naive Bayes, MaxEnt, part-of-speech tagging, feature engineering, hidden Markov models, generative and discriminative models, Latent Dirichlet Allocation (LDA), bag-of-words models, vector space similarity, and cosine similarity. These concepts and algorithms play a crucial role in various NLP tasks, such as document classification, part-of-speech tagging, and semantic similarity.