The following sections provide descriptions of important text analysis terms.
Analyzers tell OpenSearch how to index and search text. An analyzer is composed of three components: a tokenizer, zero or more token filters, and zero or more character filters.
OpenSearch provides built-in analyzers. For example, the
standard built-in analyzer converts text to lowercase and breaks text into tokens based on word boundaries such as carriage returns and white space. The
standard analyzer is also called the default analyzer and is used when no analyzer is specified in the text analysis request.
If needed, you can combine tokenizers, token filters, and character filters to create a custom analyzer.
Tokenizers break unstuctured text into tokens and maintain metadata about tokens, such as their start and ending positions in the text.
Character filters examine text and perform translations, such as changing, removing, and adding characters.
Token filters modify tokens, performing operations such as converting a token’s characters to uppercase and adding or removing tokens.
Similar to analyzers, normalizers tokenize text but return a single token only. Normalizers do not employ tokenizers; they make limited use of character and token filters, such as those that operate on one character at a time.
By default, OpenSearch does not apply normalizers. To apply normalizers, you must add them to your data before creating an index.