Statistical, ML and Neural Models of POS Tagging

Statistical, ML and Neural Models of POS Tagging #

‘HMM Inference: Forward and Viterbi Algorithms’ covers the portion:

• Forward Algorithm
• Viterbi Algorithm
• HMM inference for POS tagging

The complete Topic also includes:

• Maximum Entropy Markov Models
• Bidirectionality
• Neural-network models for POS tagging

HMM Inference: Forward and Viterbi Algorithms #

Hidden Markov Models create two closely related inference problems:

• Likelihood: How probable is an observed sequence under the model?
• Decoding: Which hidden-state sequence most probably generated the observations?

The Forward Algorithm solves the likelihood problem, while the Viterbi Algorithm solves the decoding problem. Both use dynamic programming and a trellis, but they combine paths differently.