ngrambaserte

Ngrambaserte refers to approaches that rely on n-grams—contiguous sequences of n items—drawn from linguistic data. An n-gram can be a sequence of characters or words, and the method builds statistical models from how frequently these sequences occur in a corpus.

Typically, an n-gram model estimates the probability of a unit given its preceding n−1 units, using relative frequencies and smoothing to handle n-grams that do not appear in the training data. These models are often evaluated by perplexity or accuracy in a given setting.

Applications include language modeling, spelling and grammar checking, OCR post-processing, autocomplete and search ranking, text classification, authorship attribution, and plagiarism detection.

There are two main variants: character-level n-grams and word-level n-grams. Character n-grams capture subword patterns and are robust to rare or unknown words, while word n-grams model syntactic and semantic patterns more directly. The choice of n involves a trade-off between context and data sparsity.

Historically, n-gram models were foundational in natural language processing during the 1980s–2000s and remain useful in resource-limited settings or as components in hybrid systems. They are often faster and more explainable than neural approaches, though less capable of modeling long-range dependencies.

Strengths include interpretability, simplicity, and low computational requirements; limitations include sparsity for large n, limited context to n−1 prior items, and difficulty capturing long-range dependencies without enormous data.

See also: N-gram, language model, smoothing (statistics), Katz backoff, Kneser-Ney smoothing, Markov model.