Which describes a problem with n-grams?

Which describes a problem with n-grams?

• A. They only look back a certain number of words which can lead to grammar errors.
• B. They only look forward to future words.
• C. They always produce perfect grammar.
• D. They rely on semantic similarity.

Answer: (A)

N-gram models rely on a fixed-length context, looking back a set number of words to predict the next one. This limited window means they can’t reliably capture longer-range grammatical dependencies, so they may produce errors when the correct form depends on something beyond that window. That’s the main issue: the fixed past context leads to grammar problems in cases where understanding long-distance structure is needed. The other statements don’t fit: n-grams don’t look forward to future words in the standard setup, they don’t guarantee perfect grammar, and they rely on observed word co-occurrence rather than true semantic understanding.

N-gram models rely on a fixed-length context, looking back a set number of words to predict the next one. This limited window means they can’t reliably capture longer-range grammatical dependencies, so they may produce errors when the correct form depends on something beyond that window. That’s the main issue: the fixed past context leads to grammar problems in cases where understanding long-distance structure is needed. The other statements don’t fit: n-grams don’t look forward to future words in the standard setup, they don’t guarantee perfect grammar, and they rely on observed word co-occurrence rather than true semantic understanding.