N-gram Language Model
An N-gram language model is a probabilistic model used in natural language processing (NLP) that predicts the next word in a sequence based on the previous N-1 words. It operates by calculating the probability of a word given its context, using frequency counts from a training corpus to estimate these probabilities. This simple yet effective approach is foundational for tasks like text generation, speech recognition, and machine translation.
Developers should learn N-gram models when working on basic NLP applications, such as autocomplete features, spelling correction, or simple chatbots, as they provide a straightforward way to model language patterns with minimal computational overhead. They are particularly useful in scenarios where data is limited or when building lightweight systems, though they have largely been superseded by neural models for complex tasks.