Knowledge Discovery 26.3
Knowledge Discovery Expert

# Tokenization

The Content component stores document text as a series of tokens. Generally, a token is a word, but it can also include other strings of characters (such as a phone number or e-mail address).

During the index process, Content converts the text into tokens for matching, and stores it in Index fields. The following sections describe how Contentconverts text into tokens, and how you can manipulate the results.

## Types of Characters

Content processes characters according to their common use, and uses them to define tokens in text.

Content contains a default set of definitions, which is appropriate in most cases. You might want to change the default definitions if your data contains special strings that you want to search for, such as e-mail addresses. See Example: E-mail Addresses.

In Content, the following three types of character define tokens and the breaks between tokens:

Character type | Description
--- | ---
Text character | Letters and numbers, including logograph characters from Asian writing systems.
Separator character | Characters that separate two words, such as spaces, tabs, and line breaks.
Non-separator character | Other characters, such as punctuation.

When you index data into Content:

Text characters | Do not change.
Separator characters | Become spaces, which mark the break between one token and the next.
Non-separator characters | Are deleted. If text is separated only by non-separator characters, it becomes a single token.

## Example: E-mail Addresses

Consider the following e-mail address:

    joe.smith@example.com

In the default Content configuration:

- The at symbol (@) is a separator character.

- The period (`.`) is a non-separator character.

When Content processes the e-mail address, it produces two tokens: `JOESMITH` and `EXAMPLECOM`, which you can search for to return this document.

However, if you search for `JOE` or `EXAMPLE`, this document does not return. If you need to be able to search for these terms, you must alter the default configured separator characters. For this example, you define a period (`.`) as a separator character.

To add characters to the list of text characters, use the `TangibleCharacters` configuration parameter. For example, for indexing social media documents, you might want to be able to include a hashtag (#) or @ symbol. For more information, refer to the Content Component Help.

## Hyphen Characters

You can use the additional setting `HyphenChars` to index both the subparts and the whole of a hyphenated word.

For example, if you set `HyphenChars=.` then the text:

    email joe.smith

is tokenized into four terms, `EMAIL`, `JOE`, `SMITH`, and `JOESMITH`. Searches for Joe, Joe Smith, Joe.Smith, and joesmith all match the document.

However, the subparts of the hyphenated terms are indexed without a position. This means that you cannot match them with a phrase or proximity query. For example, for the text above, a query for the phrase email joe does not match.

For this reason, OpenText recommends that you turn hyphenation off in almost all cases, by setting `HyphenChars` to `NONE`, and instead use `AugmentSeparators`. For example:

    AugmentSeparators=.

This configuration matches all the same queries, except for joesmith, which is a rarely used query. The benefits of `AugmentSeparators` generally greatly outweigh the value of this type of query.

## Number Punctuation

The `NumberPunctuation` option specifies characters that must be treated differently in numeric and alphabetic terms. The setting defines characters to treat as `TangibleCharacters` when they appear with a numeric character on either side. For example, if the period (`.`) is a separator:

- `123.com` is tokenized as `123` and `COM`.

- `3.14` is tokenized as `3.14`.

TIP: You can set the `NumberPunctuation` character individually for different languages.

## Other Topics in this Section

- Determine the Tokens to Index

- Linguistic Adjustments

- Advanced Tokenization

Related Topics

- Order of Language Processes

Copyright 2026 Open Text
Version 26.3 | Last updated July 2026

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Product: Knowledge Discovery 26.3

Topic Title: Tokenization