Regular Expressions, Corpora Text Normalization, Minimum Edit Distance.
N-gram Language Models : N-Grams, Evaluating Language Models, Generalization and Zeros, Smoothing, Kneser-Ney Smoothing, The Web and Stupid Backoff, Advanced: Perplexit's Relation to Entropy.

Classification: the sigmoid, Learning in LR, the cross-entropy loss function, Gradient Descent, Regularization, Multinomial logistic regression, interpreting models, Deriving the Gradient Equation.Vector Semantics : Lexical Semantics, Vector Semantics, Words,Vectors, Cosine for measuring similarity, TF-IDF, Weighing terms in the vector, Applications of the tf-idf vector model, PMI, Word2vec, Visualizing Embeddings, Semantic properties, Bias,Embeddings, Evaluating Vector Models.

English Word Classes,The Penn Treebank Part-of-Speech Tagset, Part-of-Speech Tagging, HMM PoS Tagging, Maximum Entropy Markov Models, Bidirectionality, Part-of-Speech Tagging for Other Languages.
Sequence Processing with Recurrent Networks : Simple Recurrent Networks, Applications of RNNs, Deep Networks: Stacked and Bidirectional RNNs, Managing Context in RNNs, LSTMs and GRUs, Words, Characters and Byte-Pairs.

Probabilistic Context-Free Grammars, Probabilistic CKY Parsing of PCFGs, Ways to Learn PCFG Rule Probabilities, Problems with PCFGs, Improving PCFGs by Splitting Non-Terminals, Probabilistic Lexicalized CFGs, Probabilistic CCG Parsing, Evaluating Parsers, Human Parsing.
Dependency Parsing : Dependency Relations, Dependency Formalisms, Dependency Treebanks, Transition-Based Dependency Parsing, Graph-Based Dependency Parsing, Evaluation.

Named Entity Recognition, Relation Extraction, Extracting Times, Extracting Events and their Times, Template Filling.