A Bag of N-Grams is a statistical language model used in Natural Language Processing (NLP) that represents text as a collection of contiguous sequences of ‘n’ words or tokens. In this model, the order of words is significant, as it captures the context and structure of the language. The term ‘n-gram’ refers to the number of items in the sequence: for example, a 1-gram (or unigram) consists of single words, a 2-gram (or bigram) consists of pairs of consecutive words, and a 3-gram (or trigram) consists of triplets of words.
The Bag of N-Grams model is useful for various NLP tasks, including text classification, sentiment analysis, and language modeling. It allows for the analysis of word co-occurrence patterns and helps to estimate the probability of a word given its context. This is achieved by counting the frequency of each n-gram in a given corpus and using these counts to inform decisions about the text being processed.
One of the advantages of the Bag of N-Grams approach is its simplicity and effectiveness in capturing local context. However, it also has limitations, such as ignoring long-range dependencies and requiring substantial amounts of data to accurately represent rare n-grams. Despite these drawbacks, it remains a foundational technique in NLP and is often used as a baseline model against which more complex approaches are compared.