Viterbi Study guides, Class notes & Summaries

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UNIT II:Word Level Analysis:Unsmoothed N-grams, Evaluating N-grams, Smoothing, Interpolation and Backoff – Word Classes, Part-of-Speech Tagging, Rule-based, Stochastic and Transformation-based tagging, Issues in PoS tagging – Hidden Markov and Maximum Entropy models,Viterbi algorithms and EM training.

The document includes the following topics - UNIT I:Introduction: Origins and challenges of NLP – Language Modeling: Grammar based LM, Statistical LM – Regular Expressions, Finite-State Automata – English Morphology, Transducers for lexicon and rules, Tokenization, Detecting and Correcting Spelling Errors, Minimum Edit Distance. UNIT II:Word Level Analysis:Unsmoothed N-grams, Evaluating N-grams, Smoothing, Interpolation and Backoff – Word Classes, Part-of-Speech Tagging, Rule-based, ...