Hidden Markov Model (HMM) – MCQs, Notes & Practice Questions | ExploreDatabase

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Key Concepts Illustrated in the Figure

1. Visible states (Observations)
   Visible states are the observed outputs of an HMM, such as words in a sentence. In the above figure, 'cat', 'purrs', etc are observations.
2. Hidden states
   Hidden states are the unobserved underlying states (e.g., POS tags - 'DT', 'N', etc in the figure) that generate the visible observations.
3. Transition probabilities
   Transition probabilities define the likelihood of moving from one hidden state to another. In the figure, this is represented by the arrows from one POS tag to the other. Example: P(N -> V) or P(V | N).
4. Emission probabilities
   Emission probabilities define the likelihood of a visible observation being generated by a hidden state. In the figure, this is represented by the arrows from POS tags to words. Example: P(cat | N).
5. POS tagging using HMM
   POS tagging using HMM models tags as hidden states and words as observations to find the most probable tag sequence.
6. Evaluation problem
   The evaluation problem computes the probability of an observation sequence given an HMM.
7. Forward algorithm
   The forward algorithm efficiently solves the evaluation problem using dynamic programming.
8. Decoding problem
   The decoding problem finds the most probable hidden state sequence for a given observation sequence.

1. In POS tagging using HMM, the hidden states represent:

Words in the sentence
POS tags assigned to each word
Dependency relations
Lemmatized tokens

Correct Answer: B

In HMM-based POS tagging, tags are hidden states and words are observed symbols.

2. The most suitable algorithm for decoding the best POS sequence in HMM tagging is:

Forward algorithm
Backward algorithm
Baum–Welch algorithm
Viterbi algorithm

Correct Answer: D

Viterbi decoding finds the most probable hidden tag sequence.

3. Transition probabilities in HMM POS tagging define:

P(word | tag)
P(tag_t | tag_t−1)
P(sentence | corpus)
P(tag | word frequency)

Correct Answer: B

Transition probability models tag-to-tag dependency.

4. Emission probability in POS tagging refers to:

Probability of tag transition
Probability of sentence structure
Probability of word given a tag
Probability of next two tags jointly

Correct Answer: C

Emission probability is P(word | tag).

5. Which problem does Baum–Welch training solve in HMM POS tagging?

POS disambiguation
Optimal decoding
Learning parameters from unlabeled text
Removing rare tags

Correct Answer: C

Baum–Welch (EM) learns transition and emission probabilities without labeled data.

6. If an HMM POS tagger has 50 tags and a 20,000-word vocabulary, the emission matrix size is:

20,000 × 20,000
50 × 20,000
20,000 × 50
50 × 50

Correct Answer: B

Rows correspond to tags and columns to words.

7. A trigram HMM POS tagger models:

P(tag_t | tag_t−1)
P(tag_t | tag_t−1, tag_t−2)
P(word | tag)
P(word_t | word_t−1)

Correct Answer: B

Trigram models capture dependency on two previous tags.

8. Data sparsity in emission probabilities mostly occurs due to:

Too many hidden states
Unseen words in test data
Transition collapse
Deterministic decoding

Correct Answer: B

Unseen words lead to zero emission probabilities without smoothing.

9. A common solution for unknown words in HMM POS tagging is:

Random assignment
Laplace or Good–Turing smoothing
Removing words
Ignoring emissions

Correct Answer: B

Smoothing assigns non-zero probabilities to unseen events.

10. POS tagging is considered a:

Generation task
Sequence labeling task
Knowledge-graph task
Alignment task

Correct Answer: B

Each token is labeled sequentially → classic sequence labeling.