> ## Documentation Index
> Fetch the complete documentation index at: https://docs.abbyy.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Named Entities (NER) activity

> Extract named entities — people, organizations, locations, addresses, money, dates, durations — from unstructured documents using NLP in Advanced Designer.

The Named Entities (NER) activity is designed to use Natural Language Processing (NLP) to extract named entities from unstructured documents, such as contracts, letters, orders, press releases, and other documents with no specific structure that can be described using rules. To process these documents using a Named Entities (NER) activity, you need to map the named entities to the skill fields into which the entity values will be extracted. This activity will then analyze the document and extract the named entities into their corresponding fields.

You can also set up named entity extraction for fields extracted by other activities. Suppose you know that organization names and addresses that you need to extract are located in the first paragraph of each contract. You can extract the first paragraph using a Segmentation activity, and then extract company names and addresses from this paragraph using a Named Entities (NER) activity. This approach is more reliable than extracting named entities from the entire document, since you can control the specific area where those entities are extracted from.

<Note>
  The activity only supports fields of type Text that have data type set to Text, Date, or Money.
</Note>

## Set up a Named Entities (NER) activity

<Steps>
  <Step title="Add the activity">
    On the **Activities** tab, add a Named Entities (NER) activity to the document processing flow.
  </Step>

  <Step title="Select the source">
    On the **Activity Properties** pane, use the **Source** drop-down list to select a source that the activity will use to extract named entities from — either the whole document or a single field extracted by another activity.
  </Step>

  <Step title="Select output fields">
    In the **Output field**, select fields into which the named entities will be extracted.

    The output fields must be either on the same nesting level as the source field or one level below it.
  </Step>

  <Step title="Create the mapping">
    Click **Create Mapping**. In the dialog that opens, select which named entities will be extracted to each field in the **Entity to extract** list. Click **Save**. You can edit the mapping at any time by clicking **Edit Mapping**.
  </Step>

  <Step title="Test the skill">
    Click **Test Skill** to run the skill and review the named entity extraction results on the **Results** tab.
  </Step>
</Steps>

## Supported named entities

| Entity name      | Description            | Example                                                                       | Supported data types          | Supported languages                                                                        |
| :--------------- | :--------------------- | :---------------------------------------------------------------------------- | :---------------------------- | :----------------------------------------------------------------------------------------- |
| **Person**       | Names of people        | John Doe, Jane Smith                                                          | **Text**                      | English, Russian, German, French, Spanish, Japanese, Italian, Portuguese (Standard), Dutch |
| **Location**     | Names of locations     | Anytown, Corporate Place                                                      | **Text**                      | English, Russian, German, French, Spanish, Japanese, Italian, Portuguese (Standard), Dutch |
| **Organization** | Names of organizations | ABBYY, Acme Corp.                                                             | **Text**                      | English, Russian, German, French, Spanish, Japanese, Italian, Portuguese (Standard), Dutch |
| **Address**      | Addresses              | 123 Main Str., Anytown AB 45678, 950 Acacia Avenue 50, Anytown, AB 12345, USA | **Text**                      | English, Russian, German, French, Spanish, Japanese, Italian, Portuguese (Standard), Dutch |
| **Money**        | Amounts of money       | \$2670.00, 199 dollars 99 cents                                               | **Text**, **Amount of money** | English, Russian, German, French, Spanish, Japanese, Italian, Portuguese (Standard), Dutch |
| **Date**         | Dates                  | November 14, 2009, 11/14/2009                                                 | **Text**, **Date**            | English, Russian, German, French, Spanish, Japanese, Italian, Portuguese (Standard), Dutch |
| **Duration**     | Time periods           | Twelve (12) months, 4 days                                                    | **Text**                      | English, Russian, German, French, Spanish, Italian, Portuguese (Standard), Dutch           |