POS Tagging - Viterbi Decoding
What is the primary purpose of the Viterbi algorithm in Natural Language Processing?
In the context of POS tagging, what do the 'hidden states' represent in a Hidden Markov Model?
What information does the emission matrix provide in HMM-based POS tagging?
What does the transition matrix represent in the context of POS tagging?
What is the key computational technique that makes the Viterbi algorithm efficient?
In the Viterbi algorithm, what does each cell V[i][j] in the Viterbi table represent?
What is the time complexity of the Viterbi algorithm for a sentence of length N with T possible tags?
In the Viterbi algorithm, why is backtracking necessary after filling the table?
Consider a sentence 'Book a park' where 'Book' could be either a noun or verb. How does the Viterbi algorithm handle this ambiguity?