What are N-Grams?

N-grams are contiguous sequences of n items from a given sample of text or speech. The items can be phonemes, syllables, letters, words, or base pairs according to the application. N-grams are used in various areas of computational linguistics and text analysis. They are a simple and effective method for text mining and natural language processing (NLP) tasks, such as text prediction, spelling correction, language modeling, and text classification.

Understanding N-Grams

The concept of an n-gram is straightforward: it is a sequence of 'n' consecutive items. For instance, in the domain of text analysis, if 'n' is 1, we call it a unigram; if 'n' is 2, it is a bigram; if 'n' is 3, it is a trigram, and so on. The larger the value of 'n', the more context you have, but with diminishing returns on information gained versus computational expense and data sparsity.

Consider the sentence "The quick brown fox jumps over the lazy dog." Here are some examples of n-grams derived from this sentence:

  • Unigrams: "The", "quick", "brown", "fox", "jumps", "over", "the", "lazy", "dog"
  • Bigrams: "The quick", "quick brown", "brown fox", "fox jumps", "jumps over", "over the", "the lazy", "lazy dog"
  • Trigrams: "The quick brown", "quick brown fox", "brown fox jumps", "fox jumps over", "jumps over the", "over the lazy", "the lazy dog"

As you can see, unigrams do not contain any context, bigrams contain a minimal context, and trigrams start to form more coherent and contextually relevant phrases.

Applications of N-Grams

N-grams are widely used in various NLP tasks. Here are a few examples:

  • Language Modeling: N-grams can be used to predict the next item in a sequence, making them useful for language models in speech recognition, typing prediction, and other generative tasks.
  • Text Classification:

    They can serve as features for algorithms that classify documents into categories, such as spam filters or sentiment analysis.

  • Machine Translation:

    N-grams help in statistical machine translation systems by providing probabilities of sequences of words appearing together.

  • Spell Checking and Correction: They can be used to suggest corrections for misspelled words based on the context provided by surrounding words.
  • Information Retrieval: Search engines use n-grams to index texts and provide search results based on the likelihood of n-gram sequences.

Challenges with N-Grams

While n-grams are a powerful tool, they come with their own set of challenges:

  • Data Sparsity: As 'n' increases, the frequency of specific n-gram sequences decreases, leading to data sparsity issues. This can make it difficult for models to learn effectively from such sparse data.
  • Computational Complexity: The number of possible n-grams increases exponentially with 'n', which can lead to increased computational costs and memory requirements.
  • Context Limitation: N-grams have a fixed context window of 'n' items, which may not be sufficient to capture longer dependencies in the text.

Smoothing Techniques

To address data sparsity and improve the performance of n-gram models, various smoothing techniques are employed. These techniques adjust the probability distribution of n-grams to account for unseen or rare n-gram sequences. Some common smoothing techniques include:

  • Additive or Laplace smoothing
  • Good-Turing discounting
  • Backoff and interpolation methods


N-grams are a fundamental concept in NLP and text analysis that provide a simple yet powerful way to represent text data for various tasks. They are a cornerstone of many classical language processing approaches and continue to be relevant in the era of deep learning, often serving as baseline features for more complex models. Despite their limitations, n-grams are an essential tool in the arsenal of any data scientist or NLP practitioner.

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