To understand and demonstrate the application of the Viterbi algorithm for Part-of-Speech (POS) tagging in Natural Language Processing. This experiment provides hands-on experience with the Viterbi decoding process, which is a fundamental dynamic programming algorithm used to find the most likely sequence of hidden states (POS tags) given observable sequences (words) in Hidden Markov Models.

The Viterbi algorithm is crucial in statistical NLP for solving the decoding problem: given a sequence of words and pre-computed emission and transition probabilities from a training corpus, determine the most probable sequence of POS tags that generated those words. This experiment allows learners to practice filling Viterbi tables step-by-step and understand how dynamic programming efficiently finds optimal tag sequences.

For example, given the sentence "Book a park", the algorithm determines whether "Book" should be tagged as a noun or verb, considering both:

• Emission probabilities: How likely each word is to be generated by each POS tag
• Transition probabilities: How likely each POS tag is to follow another in sequence

Through interactive simulation, learners will master the mathematical foundations of the Viterbi algorithm and its practical application in modern POS tagging systems.