How is a Markov model described as related to n-grams in the provided description?

How is a Markov model described as related to n-grams in the provided description?

• A. A Markov model is a probabilistic model based on transitions.
• B. A Markov model is equivalent to an n-gram model.
• C. An n-gram model is more general than a Markov model.
• D. Markov models use deep learning.

Answer: (B)

The idea being tested is that, in language modeling, the Markov assumption is implemented with a fixed memory window, which is exactly what an n-gram model uses. A Markov model says that the probability of the next element depends only on a limited recent history, not the entire past. When we set that memory to the previous n−1 items, the model becomes an n-gram model of order n. So describing a Markov model as equivalent to an n-gram model is appropriate in this context: a Markov process of order n−1 is captured by an n-gram model that conditions on the last n−1 tokens. For example, a trigram model predicts the next word based on the two preceding words, which is a second-order Markov process in this framing. In broader terms, Markov models can be more general than fixed-length n-grams, but within the common NLP framing they are essentially the same idea.

The idea being tested is that, in language modeling, the Markov assumption is implemented with a fixed memory window, which is exactly what an n-gram model uses. A Markov model says that the probability of the next element depends only on a limited recent history, not the entire past. When we set that memory to the previous n−1 items, the model becomes an n-gram model of order n. So describing a Markov model as equivalent to an n-gram model is appropriate in this context: a Markov process of order n−1 is captured by an n-gram model that conditions on the last n−1 tokens. For example, a trigram model predicts the next word based on the two preceding words, which is a second-order Markov process in this framing. In broader terms, Markov models can be more general than fixed-length n-grams, but within the common NLP framing they are essentially the same idea.