1What is the primary goal of calculating Minimum Edit Distance?
A.To find the longest common subsequence between two strings
B.To calculate the probability of a word appearing in a sentence
C.To generate word embeddings for a neural network
D.To quantify the dissimilarity between two strings by counting operations
Correct Answer: To quantify the dissimilarity between two strings by counting operations
Explanation:
Minimum Edit Distance measures the minimum number of editing operations (insertion, deletion, substitution) needed to transform one string into another.
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2Which of the following operations is NOT typically used in the Levenshtein distance algorithm?
A.Substitution
B.Insertion
C.Deletion
D.Transposition
Correct Answer: Transposition
Explanation:
Standard Levenshtein distance allows insertion, deletion, and substitution. Damerau-Levenshtein adds transposition.
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3In the context of dynamic programming for edit distance, if source[i] equals target[j], what is the cost of substitution?
A.Infinity
B.1
C.2
D.0
Correct Answer: 0
Explanation:
If the characters are identical, no substitution is needed, so the cost added is 0.
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4What is the Minimum Edit Distance between the strings 'cat' and 'cut'?
A.3
B.0
C.1
D.2
Correct Answer: 1
Explanation:
One operation is required: substitute 'a' with 'u'.
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5How does an autocorrect system typically identify candidate words for a misspelled word?
A.By selecting random words from the dictionary
B.By using the longest word in the dictionary
C.By choosing words that start with the same letter only
D.By finding words within a certain edit distance threshold
Correct Answer: By finding words within a certain edit distance threshold
Explanation:
Autocorrect generates candidates by looking for words in the dictionary that are 1 or 2 edit distances away from the misspelled input.
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6Which algorithm is commonly used to efficiently calculate the Minimum Edit Distance?
A.K-Means Clustering
B.Depth-First Search
C.Gradient Descent
D.Dynamic Programming
Correct Answer: Dynamic Programming
Explanation:
Dynamic programming is used to fill a matrix of costs to find the optimal path of operations.
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7In Part of Speech (POS) tagging, what is the 'Hidden' component in a Hidden Markov Model?
A.The transition probabilities
B.The punctuation marks
C.The Part of Speech tags
D.The words in the sentence
Correct Answer: The Part of Speech tags
Explanation:
In an HMM for POS tagging, the tags (states) are hidden, and the words (observations) are visible.
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8What does the Markov Assumption state in the context of Markov Chains?
A.The future state depends on all past states
B.The future state depends on the hidden emissions
C.The future state depends only on the current state
D.The future state is independent of the current state
Correct Answer: The future state depends only on the current state
Explanation:
The Markov Assumption (specifically for a first-order chain) assumes that the probability of the next state depends only on the current state, not the entire history.
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9What are 'Transition Probabilities' in an HMM?
A.The probability of a word being misspelled
B.The probability of a sentence starting with a specific word
C.The probability of moving from one POS tag to another
D.The probability of generating a specific word given a tag
Correct Answer: The probability of moving from one POS tag to another
Explanation:
Transition probabilities define the likelihood of a sequence of states, such as a Noun following a Determiner.
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10What are 'Emission Probabilities' in an HMM used for POS tagging?
A.P(tag|previous_tag)
B.P(tag|word)
C.P(word|previous_word)
D.P(word|tag)
Correct Answer: P(word|tag)
Explanation:
Emission probability is the likelihood of observing a specific word given the current hidden state (POS tag).
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11Which algorithm is used to find the most likely sequence of hidden states (POS tags) given a sequence of observations?
A.The Edit Distance Algorithm
B.The Viterbi Algorithm
C.The Backward Algorithm
D.The Forward Algorithm
Correct Answer: The Viterbi Algorithm
Explanation:
The Viterbi algorithm is a dynamic programming algorithm used for decoding: finding the most probable sequence of hidden states.
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12A text corpus is:
A.A large, structured set of texts used for statistical analysis
B.A set of rules for grammar
C.A dictionary of word definitions
D.A software used for autocorrection
Correct Answer: A large, structured set of texts used for statistical analysis
Explanation:
A corpus is a large body of text used to train language models, calculate probabilities, and analyze linguistic patterns.
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13In an N-gram language model, what is 'N'?
A.The number of words in the sentence
B.The number of hidden states
C.The dimension of the word embedding
D.The number of words in the sequence considered for probability
Correct Answer: The number of words in the sequence considered for probability
Explanation:
N represents the size of the window of words. A bigram has N=2, looking at the current word and one previous word.
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14Which assumption simplifies the calculation of N-gram probabilities?
A.The probability depends on the entire sentence history
B.The probability of a word depends only on the previous N-1 words
C.All words have equal probability
D.Words are independent of each other
Correct Answer: The probability of a word depends only on the previous N-1 words
Explanation:
This is the Markov assumption applied to N-gram models to make computation feasible.
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15How is the probability of a bigram P(w2 | w1) calculated from a corpus?
A.Count(w1) * Count(w2)
B.Count(w1, w2) / Count(w2)
C.Count(w1, w2) / Count(w1)
D.Count(w1) / Count(w2)
Correct Answer: Count(w1, w2) / Count(w1)
Explanation:
The probability of w2 following w1 is the count of the pair (w1, w2) divided by the total count of the history word w1.
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16What is the main problem with N-gram models when N is very large?
A.The vocabulary size decreases
B.The model becomes too simple
C.Data sparsity (many sequences have zero counts)
D.The context window becomes too small
Correct Answer: Data sparsity (many sequences have zero counts)
Explanation:
As N increases, the number of possible combinations grows exponentially, and most specific sequences will not appear in the training corpus.
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17What technique is used to handle N-grams that have zero probability in the training data?
Smoothing techniques like Add-1 (Laplace) assign a small non-zero probability to unseen N-grams to prevent zero-probability errors.
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18In Laplace (Add-1) smoothing, what is added to the denominator?
A.The number of sentences
B.The total word count
C.1
D.The vocabulary size (V)
Correct Answer: The vocabulary size (V)
Explanation:
To normalize the probability after adding 1 to the numerator of every word type, the vocabulary size V must be added to the denominator.
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19What does an autocomplete system try to maximize?
A.The length of the sentence
B.P(previous_words | next_word)
C.The edit distance between words
D.P(next_word | previous_words)
Correct Answer: P(next_word | previous_words)
Explanation:
Autocomplete predicts the most likely next word given the context of previous words.
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20A Trigram model looks at how many previous words to predict the next word?
A.3
B.2
C.0
D.1
Correct Answer: 2
Explanation:
A trigram involves 3 words total (the target and 2 history words), so it looks at the previous 2 words.
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21One-hot encoding of words results in vectors that are:
A.Sparse and high-dimensional
B.Sparse and low-dimensional
C.Dense and high-dimensional
D.Dense and low-dimensional
Correct Answer: Sparse and high-dimensional
Explanation:
One-hot vectors have a size equal to the vocabulary (high-dimensional) and contain mostly zeros (sparse).
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22What is a major limitation of one-hot encoding for words?
A.It is difficult to compute
B.It cannot represent rare words
C.It does not capture semantic similarity between words
D.It requires a neural network
Correct Answer: It does not capture semantic similarity between words
Explanation:
In one-hot encoding, all word vectors are orthogonal, so the distance between 'car' and 'bus' is the same as 'car' and 'apple'.
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23Word embeddings typically represent words as:
A.Sparse binary vectors
B.Dense vectors of real numbers
C.Strings
D.Integers
Correct Answer: Dense vectors of real numbers
Explanation:
Embeddings are dense vectors (e.g., length 300) containing real numbers that capture semantic meaning.
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24Which metric is commonly used to measure the similarity between two word embedding vectors?
A.Edit Distance
B.Perplexity
C.Cosine Similarity
D.Jaccard Index
Correct Answer: Cosine Similarity
Explanation:
Cosine similarity measures the cosine of the angle between two vectors, indicating how close they are in the semantic space.
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25The Word2Vec model 'Skip-gram' architecture tries to predict:
A.The next sentence
B.The target word given the context words
C.The POS tag of the word
D.The context words given the target word
Correct Answer: The context words given the target word
Explanation:
Skip-gram takes a central target word and tries to predict the surrounding context words.
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26The Word2Vec model 'CBOW' (Continuous Bag of Words) architecture tries to predict:
A.The context words given the target word
B.The target word given the context words
C.The part of speech
D.The document topic
Correct Answer: The target word given the context words
Explanation:
CBOW takes the surrounding context words and sums their vectors to predict the missing center word.
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27What famous algebraic property is often cited to demonstrate the semantic capability of word embeddings?
A.King - Man + Woman = Queen
B.Fast + Slow = Speed
C.Apple + Orange = Fruit
D.Paris - France = Germany
Correct Answer: King - Man + Woman = Queen
Explanation:
This vector operation demonstrates that embeddings capture gender and royalty relationships.
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28In the 'Noisy Channel Model' for spelling correction, P(x|w) represents:
A.The probability of typing x given the intended word w
B.The probability of x being a valid word
C.The probability of the word w appearing in the corpus
D.The probability that the user meant w but typed x
Correct Answer: The probability of typing x given the intended word w
Explanation:
P(x|w) is the error model probability: the likelihood of generating the typo 'x' when the intended word was 'w'.
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29When building an HMM for POS tagging, the sum of probabilities of all outgoing transitions from a single state must equal:
A.The number of states
B.1
C.The number of observations
D.0
Correct Answer: 1
Explanation:
Probabilities are normalized; the sum of probabilities of transitioning to any possible next state must be 1.
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30What is 'Perplexity' in the context of Language Models?
A.A measure of how well a probability model predicts a sample
B.The size of the vocabulary
C.The number of parameters in the model
D.The time taken to train the model
Correct Answer: A measure of how well a probability model predicts a sample
Explanation:
Perplexity is an intrinsic evaluation metric. Lower perplexity indicates a better model.
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31Why do we use Log Probabilities instead of raw probabilities in N-gram calculations?
A.To make numbers larger
B.To increase perplexity
C.To handle negative numbers
D.To avoid arithmetic underflow
Correct Answer: To avoid arithmetic underflow
Explanation:
Multiplying many small probabilities results in extremely small numbers that computers cannot represent accurately (underflow). Adding logs avoids this.
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32Which of the following describes the 'Start' token (<s>) in N-gram models?
A.It represents an unknown word
B.It is used for punctuation
C.It indicates the end of a sentence
D.It gives context for the first word in the sentence
Correct Answer: It gives context for the first word in the sentence
Explanation:
The start token allows the model to calculate the probability of a word being at the beginning of a sentence.
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33What represents the 'Observations' in a POS HMM?
A.The sequence of words in the text
B.The sequence of tags
C.The initial state probabilities
D.The transition matrix
Correct Answer: The sequence of words in the text
Explanation:
In an HMM for POS tagging, we observe the words and try to infer the hidden tags.
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34In Minimum Edit Distance, the 'backtrace' step is used to:
A.Sum the rows
B.Calculate the cost
C.Determine the actual sequence of operations (alignment)
D.Initialize the matrix
Correct Answer: Determine the actual sequence of operations (alignment)
Explanation:
While the forward pass calculates the cost, backtracing from the bottom-right of the matrix reveals which operations led to that cost.
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35Which token is typically used to replace words not found in the training vocabulary?
A.<END>
B.<START>
C.<UNK>
D.<NULL>
Correct Answer: <UNK>
Explanation:
<UNK> (Unknown) is the standard token used to represent out-of-vocabulary words.
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36A 'Unigram' model assumes that:
A.Words depend on the grammar
B.Words depend on the previous word
C.Words are independent of context
D.Words depend on the previous two words
Correct Answer: Words are independent of context
Explanation:
A unigram model calculates probability based solely on word frequency, ignoring surrounding context.
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37The dimensionality of a Word2Vec embedding vector is typically chosen by:
A.The length of the sentence
B.The size of the vocabulary
C.The system designer (hyperparameter)
D.The number of unique characters
Correct Answer: The system designer (hyperparameter)
Explanation:
The dimension (e.g., 100, 300) is a design choice, unlike one-hot vectors which are fixed by vocabulary size.
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38In the equation P(tag|word) ∝ P(word|tag) * P(tag), what is P(tag)?
A.Posterior probability
B.Prior probability
C.Likelihood
D.Emission probability
Correct Answer: Prior probability
Explanation:
P(tag) represents the prior probability of the tag occurring, typically approximated by the transition probability from the previous tag.
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39If we want to build a spell checker, which probability do we want to maximize according to Bayes' theorem?
A.P(correction)
B.P(typo | correction)
C.P(correction | typo)
D.P(typo)
Correct Answer: P(correction | typo)
Explanation:
We observe the typo and want to find the most probable correction given that typo.
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40Which type of language model suffers most from the 'curse of dimensionality'?
A.Bag of Words
B.Word2Vec
C.High-order N-gram model (e.g., 5-gram)
D.Unigram model
Correct Answer: High-order N-gram model (e.g., 5-gram)
Explanation:
As N increases, the number of parameters grows exponentially, requiring exponentially more data to train effectively.
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41What is the primary input to a neural network training a Word2Vec model?
A.Image pixels
B.One-hot encoded vectors of words
C.Parse trees
D.Audio signals
Correct Answer: One-hot encoded vectors of words
Explanation:
The input layer usually receives one-hot vectors, which are then multiplied by the weight matrix to get the embedding.
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42The term 'corpus' in NLP refers to:
A.A specific neural network layer
B.A computer algorithm
C.A type of spelling error
D.A body of text data
Correct Answer: A body of text data
Explanation:
Corpus (plural: corpora) is the raw text data used to train models.
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43In edit distance, if we assign a higher cost to substitution than insertion/deletion, it implies:
A.Typing a wrong letter is considered worse than missing a letter
B.The distance will always be zero
C.Insertion is impossible
D.The algorithm will fail
Correct Answer: Typing a wrong letter is considered worse than missing a letter
Explanation:
Costs reflect the penalty of an error; a higher cost makes that specific operation less likely to appear in the optimal path (minimum distance).
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44What is the result of using a sliding window in N-gram generation?
A.It converts text to uppercase
B.It removes stop words
C.It creates a sequence of overlapping word chunks
D.It calculates the edit distance
Correct Answer: It creates a sequence of overlapping word chunks
Explanation:
A sliding window moves over the text to extract tokens for (w-1, w) pairs used in training.
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45Which of these words likely has a vector closest to 'frog' in a well-trained embedding space?
A.Philosophy
B.Galaxy
C.Toad
D.Steel
Correct Answer: Toad
Explanation:
Embeddings capture semantic meaning; 'frog' and 'toad' share biological and contextual similarities.
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46In an HMM, what connects hidden states to each other?
A.The Viterbi path
B.Emission probabilities
C.Observation vectors
D.Transition probabilities
Correct Answer: Transition probabilities
Explanation:
Transition probabilities define the likelihood of moving from one hidden state to the next hidden state.
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47What is 'Stupid Backoff' in the context of Language Models?
A.A way to delete wrong words
B.A method to stop the algorithm
C.A type of neural network
D.A smoothing method that uses lower-order N-grams if higher-order ones are missing
Correct Answer: A smoothing method that uses lower-order N-grams if higher-order ones are missing
Explanation:
If a trigram count is zero, the model 'backs off' to use the bigram probability (often multiplied by a constant like 0.4).
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48Which application primarily utilizes Probabilistic Language Models?
A.Speech Recognition
B.Image Compression
C.Network Routing
D.Database Management
Correct Answer: Speech Recognition
Explanation:
Language models help distinguish between acoustically similar words (e.g., 'recognize speech' vs 'wreck a nice beach') based on probability.
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49In the context of Word Embeddings, what does 'Polysemy' refer to?
A.Words with similar spellings
B.Words in different languages
C.Words with multiple meanings
D.Words that rhyme
Correct Answer: Words with multiple meanings
Explanation:
Polysemy (e.g., 'bank' as a river side vs financial institution) is a challenge for static embeddings like Word2Vec, which assign one vector per word.
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50If P(A|B) is the probability of tag A following tag B, this is an example of:
A.Edit probability
B.Emission probability
C.Transition probability
D.Observation probability
Correct Answer: Transition probability
Explanation:
It describes the transition between two hidden states (tags).