Here is the detailed list of subjects from lecture 7 that we have presented in class and that will be matter of evaluation at the finals.
Lecture 07: Part-of-Speech Tagging
Content: Part-of-speech (PoS) and PoS tagging task. Evaluation for PoS tagging. Hidden Markov model (HMM), emission and transition probabilities. Probability estimation. HMM as automata. The Viterbi algorithm for decoding. The forward algorithm and the trellis data structure. Conditional random fields (CRF) and global features. Linear chain CRF, local features and feature templates. Decoding for CRF using Viterbi algorithm. Training algorithm for CRF: loss function, regularization, and stochastic gradient descent (sketch only). Neural PoS taggers using local search: fixed-window feed-forward neural model, recurrent neural model, and recurrent bidirectional model. Neural PoS taggers using global search: neural model combining RNN and CRF (sketch only). Named entity recognition and other sequence labelling tasks.
References: Jurafsky & Martin, chapter 17. Use lecture slides for forward algorithm and trellis, or else look into Jurafsky & Martin appendix A (available through the textbook web page only). Training algorithm for linear chain CRF is taken from Eisenstein, section 7.5.3. Use lecture slides for the fixed-window feed-forward neural model. Recurrent bidirectional model and neural structured prediction are taken from Eisenstein, section 7.6.1.