Ngrammodeller

ngrammodeller is a software tool and methodological approach for building n-gram language models. It represents the probability of a word sequence as conditional probabilities based on preceding n-1 words, derived from counts of n-grams observed in a text corpus.

Common features include support for word-level and character-level n-grams, configurable n, and smoothing algorithms to handle unseen n-grams. Implementations typically count all n-grams in a training corpus, then apply backoff or interpolation to estimate probabilities for lower-order histories. Smoothing options may include laplace, Good-Turing, Kneser-Ney, or Witten-Bell; some tools also offer pruning to reduce model size.

Applications include speech recognition, text prediction, spelling correction, and statistical machine translation as components of a larger system. The model can generate text by sampling from the conditional distributions, and can be evaluated with metrics such as perplexity or cross-entropy on held-out data.

Typical architecture centers on a tokenizer, an n-gram counter, a smoothing engine, and a generator. Data structures such as hash maps or tries store counts, with optional compaction for memory efficiency. Limitations include sparsity with large n and domain dependence; the models are fast and transparent relative to neural models but may require large corpora for good coverage.