N Gram Modeling
N Gram Modeling is a statistical technique for analyzing sequences of n items from text or other sequential data. An n gram is a contiguous sequence of n elements—typically words or characters—extracted from a larger corpus. By examining the frequency and patterns of these sequences in training data, n gram models estimate the probability that specific sequences will occur in new text. This probabilistic approach enables systems to predict what word or character is likely to follow a given context.
Basic Mechanism
N gram models work by counting occurrences of sequences in a training dataset and calculating conditional probabilities. For example, a bigram (2-gram) model learns which words frequently follow other words, while a trigram (3-gram) model considers sequences of three words. When encountering new text, the model uses these learned probabilities to rank likely continuations. The value of n determines the trade-off between specificity and data requirements: larger n values capture more context but require more training data to avoid sparse probability estimates.
Applications
N gram models have been foundational in natural language processing tasks including text prediction, spell-checking, machine translation, and speech recognition. They remain useful for lightweight implementations where computational efficiency is important. However, they have limitations: they cannot capture long-range dependencies or semantic relationships effectively, and they treat sequences as independent of broader meaning. Modern approaches often combine or replace n gram models with neural network-based methods like transformers, which better capture linguistic structure and context.