Statistical NLP Methods
Probabilistic and statistical approaches to NLP tasks.
Chapter 8: Text Classification Naive Bayes, logistic regression Support Vector Machines (SVMs) [Feature engineering, kernel methods, evaluation metrics: precision, recall] References Chapter 9: Topic Modeling Latent Semantic Analysis (LSA) Latent Dirichlet Allocation (LDA) [Non-negative matrix factorization, Gibbs sampling, topic coherence] References Chapter 10: Sequence Modeling Conditional Random Fields (CRFs) Applications: NER, chunking [Forward-backward algorithm, feature templates, structured prediction] References