What is a significant limitation of early N-gram language models?

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BlackTom AI · Accepted Answer

Exploring the question about early N-gram language models, we need to assess what each option claims and relate it to how N-gram models operate.
Option 1: 'They require large amounts of labeled data for training.' Traditional N-gram models do not rely on labeled data at all; they are essentially count-based models built from raw text to estimate P(w|history) based on observed frequencies. Therefore, this statement is misleading because labeling is not a core requirement of classic N-gram approaches.
Option 2: 'They are unable to capture long-range dependencies and context effectively.' This is a well-known limitation: N-gram models only consider a fixed window of preceding tokens (the n-1 history) and thus fail to model dependencies that stretch beyond that window, making them poor at capturing long-range or global context.
Option 3: 'They can only be used for text classification tasks.' In reality, N-gram models are typically used for language modeling and generation, predicting the next word, rather than being restricted to text classification. This claim misrepresents their primary use cases and capabilities, though they can be repurposed for tasks like classification with features.

Putting these assessments together, the central flaw of early N-gram models is their fixed-context limitation, which prevents learning long-range dependencies, making the second option the accurate description among the given choices.

Learning AI Through Visualization 4 Module 5 Quiz

What is a significant limitation of early N-gram language models?

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