To understand and practice sequence decoding for Part-of-Speech (POS) tagging using the Viterbi algorithm in Natural Language Processing.

This experiment aims to help students develop proficiency in applying the Viterbi algorithm to find the most probable sequence of POS tags for a given sentence, using emission and transition probabilities. Through interactive exercises, learners will gain hands-on experience with dynamic programming and sequence labeling in NLP.