N-gram
An n-gram is a contiguous sequence of n consecutive items, commonly linguistic tokens or characters, drawn from a larger sequence.
Natural Language Processing in Action, Second Edition
An n-gram is a contiguous sequence of up to n extracted elements, such as words, characters, syllables, or symbols.
- How it is used
- Uses n-grams to preserve local word order in search vectors and represent unfamiliar names through character fragments.
- How deep it goes
- Defines n-grams and demonstrates vectorizer settings and feature counts, while leaving informative n-gram selection for later treatment.
- Examples
- Tests “algorithmic bias” as a search bigram and character fragments as features of baby names.
- What you can do after
- Apply it via scikit-learn CountVectorizer or TfidfVectorizer to configure word or character n-gram features.
Read how Natural Language Processing in Action, Second Edition explains n-gram →
LLMs in Production
An n-gram is a contiguous sequence of n linguistic units, commonly used to model local patterns in text.
- How it is used
- Uses overlapping word sequences to evaluate whether generated summaries resemble reference summaries without demanding exact matches.
- How deep it goes
- Defines overlap-based scoring and distinguishes n-size variants, longest common subsequences, and skip-bigrams.
- Examples
- Uses paired Zelda summaries and Python calculations of ROUGE-1 and ROUGE-L scores.
- What you can do after
- Evaluate summary similarity with the Python rouge-score library using overlap-based metrics.
Ensemble Methods for Machine Learning
An n-gram is a contiguous sequence of n successive tokens, such as characters or words, extracted from a string.
- How it is used
- Uses n-grams to compare longer string categories and encode high-cardinality or noisy categorical features.
- How deep it goes
- Defines n-grams and places them in token-level similarity, without detailing extraction or comparison algorithms.
- Examples
- Uses employee-position titles, substring patterns, MinHashEncoder, and GapEncoder as practical examples.
- What you can do after
- Apply n-gram-based similarity through specialized encoders for noisy or high-cardinality string categories.
Read how Ensemble Methods for Machine Learning explains n-gram →
Elasticsearch in Action, Second Edition
An n-gram is a contiguous sequence of a specified number of characters or words derived from text.
- How it is used
- Supports typeahead suggestions, spelling correction, and matching broken words through overlapping character or word sequences.
- How deep it goes
- Explains character windows, edge anchoring, word shingles, tokenizer configuration, and analysis output without covering broader relevance tuning.
- Examples
- Uses “coffee” and “Bond” for character tokens, plus book titles for word shingles and typeahead search.
- What you can do after
- Apply it via Elasticsearch tokenizers and search_as_you_type mappings to inspect tokens and implement prefix-oriented search.
Read how Elasticsearch in Action, Second Edition explains n-gram →
Related concepts
- bigramincludes · A bigram is an ordered sequence of two consecutive items, such as tokens, words, or symbols, treated as a unit.
- sequence modelkind of · A sequence model processes ordered data while accounting for dependencies among its positions to produce predictions or representations.
- BLEU scoreused by · BLEU is an automatic text-generation evaluation metric that compares candidate text with reference text using modified n-gram precision and a brevity penalty.
- trigram modelincludes · A trigram model represents or predicts sequences using groups of three consecutive units, such as characters or tokens.
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