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This week's final lecture focuses on speech tagging, discussing issues and methods for sequence labeling tasks. Input sequences are discussed like sequences of words, with output being a sequence where predictions for speech tagging are made for each word. Hidden Markov Models and Maxent models are also addressed, with the use of a simple classifier for sequence labeling tasks in the Maxent model known as the Maximum Entropy Markov Model. The formulation is simple, predicting tags for each word and then multiplying probabilities for the entire sequence. The challenge lies in specifying tags for particular words.
