Virtual Labs

POS Tagging - Viterbi Decoding

After completing the Viterbi experiment, you understand that the algorithm's core principle is based on which mathematical concept?

a: Dynamic programming with optimal substructure Explanation

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b: Gradient descent optimization Explanation

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c: Random walk theory Explanation

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d: Fourier transform analysis Explanation

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In the simulation, when filling the Viterbi table for the second word onwards, what is the correct formula for computing V[i][j]?

a: V[i][j] = max_k(V[k][j-1] × transition[k][i]) × emission[i][word_j] Explanation

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b: V[i][j] = V[i][j-1] × emission[i][word_j] Explanation

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c: V[i][j] = emission[i][word_j] × transition[i][j] Explanation

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d: V[i][j] = sum_k(V[k][j-1]) × emission[i][word_j] Explanation

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Based on your experience with the simulation, what happens when you encounter a word that has zero emission probability for all tags?

a: The cell is marked as 'N/A' and effectively removed from consideration Explanation

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b: The algorithm assigns a random probability Explanation

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c: The simulation crashes Explanation

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d: All tags get equal probability Explanation

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What insight does the 'Show Answer' feature provide about your understanding of the Viterbi computation?

a: It helps identify whether errors are in probability lookup, arithmetic computation, or algorithmic understanding Explanation

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b: It shows only the final tag sequence Explanation

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c: It displays the training corpus Explanation

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d: It generates new test sentences Explanation

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From your experience with different corpora, what can you conclude about the relationship between training data and decoding outcomes?

a: Different training corpora lead to different probability distributions, affecting which tag sequences are considered most likely Explanation

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b: All corpora produce identical results regardless of content Explanation

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c: Larger corpora always produce better results Explanation

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d: The choice of corpus doesn't matter for the algorithm Explanation

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When using the simulation's hint feature, what key algorithmic insight does it emphasize about Viterbi computation?

a: Each cell depends on the maximum value from the previous column, not the sum or average Explanation

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b: All cells should be filled with random values Explanation

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c: Only emission probabilities matter Explanation

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d: The algorithm works backwards from the last word Explanation

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Consider the sentence 'Book a park' from Corpus A. If you manually changed the emission probability of P(park|verb) from 0.1 to 0.8, how would this likely affect the Viterbi decoding result?

a: The algorithm might now prefer 'park' as a verb rather than a noun, potentially changing the entire optimal sequence Explanation

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b: No change would occur since transition probabilities matter more Explanation

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c: Only the last word's tag would change Explanation

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d: The algorithm would become non-deterministic Explanation

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What computational advantage does the Viterbi algorithm provide compared to exhaustively checking all possible tag sequences?

a: It reduces complexity from exponential O(T^N) to polynomial O(N×T²), making it practical for real applications Explanation

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b: It produces more accurate results than exhaustive search Explanation

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c: It requires less memory than other approaches Explanation

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d: It can handle infinite vocabulary sizes Explanation

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Based on your simulation experience, why is it crucial to understand both the mathematical foundations and practical implementation of the Viterbi algorithm?

a: Mathematical understanding ensures correct implementation, while hands-on practice reveals edge cases and real-world constraints Explanation

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b: Only mathematical knowledge is important for algorithm design Explanation

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c: Only practical experience matters for software development Explanation

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d: The two aspects are completely independent Explanation

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