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Important :

Communications Mining is now part of UiPath IXP. Check the Introduction in the Overview Guide for more details.

Communications Mining user guide

Last updated Aug 1, 2025

Configuring Fields

Overview of setting up extraction fields

Note: Set up your labels and decide on the processes that you want to automate. Set up the extractions in one of the following ways, considering their advantages. At this stage, it is very important to decide which data points you need to extract to facilitate end-to-end automation.

Important: Make sure that a field type uses the correct data type for each field, since you cannot change the field type without losing annotations. After you change the field type, you can only manually re-annotate the field, which can be time-consuming.

Explore page

At any point during the model training process, you can set up a new extraction, modify your schema, or add any additional fields to your existing schema in Explore.
By setting up your extractions in Explore, you can:
- base your fields off data from your messages.
- add new fields to extractions as you see them.

Settings page

At any point during the model training process, you can set up a new extraction, modify your schema, or add any additional fields to your existing schema in Settings.
If you know what fields you want to extract upfront, set up your extractions in bulk, in Settings.

Train page

If you train your model through the Train tab, you can set up any new extractions. You can also annotate both labels and field extractions, as you go through the guided training experience.

General guidance

Note: The LLM generative capabilities of the platform create the extractions. Predictions are based off the trained label, and the field name.

To set up your extractions, set up your fields that require a name and a field type. It is recommended to do this at the lowest child-level label.

Be descriptive and concise. Choose field names that accurately describe the data they represent. Aim for a balance between brevity and clarity. Give your field an accurate and descriptive name, as it gives the model the necessary context on the role of the field.

For example, for an address change, if you only want to extract a new address, it is helpful to have configured field names called: new street address, new town, new postcode, and new city.

Avoid ambiguous field names. Ensure that field names are unambiguous and not easily confused with other fields or concepts in your project. For example, instead of using Value, use a more specific name like Sales Amount or Account Balance.

You can have extraction fields with the same field type in, but not for multiple general fields. To address this for general fields, create another field type with the same settings to address this.

Note: If you have a Date Change label and want to capture the Date Before and Date After fields, you cannot have the same data type tied to both these fields, for example, a Date data type used as the underlying field type for both these form definitions. As a result, you need to create two different fields types, one for Date Before and another for Date After, and map them to the respective form definitions.

Field name best practice

A Field Name is used to prompt the model. If your extractions are not performing as expected, adjust your Field Name to be more specific to your use case. Adjusting the field name may help with performance.

The following field names are just examples, so how you name your fields is use case dependent, and depends on the context of what you are trying to extract.

Use case	Not recommended Field Names	Better performing Field Names
As part of an address change request, you want to extract the details of the new address to input into your system downstream.	Address Line Postcode City	New Address Line New Postcode New City
As part of a logistics shipping request, you want to identify the total tax breakdown, both the VAT amount, and the VAT rate, on each of your goods to input into SAP.	Item ID Tax value	Item ID VAT amount VAT percentage
As part of an invoice change request, you want to identify what the old invoice number was and what it needs to be changed to, to cancel the old invoice, and re-issue a new one.	Invoice number	Old invoice number New invoice number

General vs. extraction fields

There are two different types of fields that help facilitate end-to-end automation:

General fields
Extraction fields.

It is important to understand the different types of fields available in Communications Mining, and when to use each one.

GENERAL FIELDS	EXTRACTION FIELDS
General fields are fields that you may want to extract, that can be found across multiple different topics or labels in a dataset. Generally applicable for messages across a dataset, and are not tied to a specific label. Typically useful for triaging and should be limited to data points, used as identifiers, such as policy numbers.	Extraction fields are the fields conditioned and created on a specific label. In other words, it is tied to a specific label that you want to automate. Created and trained on a message level and is tied to a specific label.

Note: When you set up your extraction schema, you need to decide what process, that is, what label, you want to automate. Your extraction schema should always contain each of the fields needed to automatically process the request.

The following table captures the key distinctions between General fields and Extraction fields. Check the differences because two completely different models predict these field kinds.

Field type	Predicted	Reviewed at	*Spanless vs.** Spanful*	Overlap spans?	Share field types between fields of same kind	Supported Data Types**
General Fields	Automatically across dataset	A paragraph level	Only spanful	No	No	String Date Monetary Quantity RegEx Template
Extraction Fields	Only on demand	A message level (in the label context)	Both spanful and spanless	Yes	Yes	String Date Monetary Quantity Number

*For more details, check Spanful field and Spanless fields.

**For more details on the data types each field supports, check Setting up field types.

Extraction Fields Example

In this example, the platform can identify the Extraction fields relevant to facilitating the end-to-end automation of these two labels.

General Fields Example

In this example, the platform is not confident enough that a certain label in the taxonomy applies to this message. The platform can still extract certain fields from the message itself. When you set up General fields, the platform can pick up these fields, irrespective of a label prediction.

Setting up fields in Explore

To set up or modify both your General fields or Extraction fields through the Explore page, apply the following steps:

Important:

Make sure that a field type uses the correct data type for each field, since you cannot change the field type without losing annotations. After you change the field type, you can only manually re-annotate the field, which can be time-consuming.

On a communication containing a label, where you want to define your extraction field in Explore, select Annotate Fields.
If you set up an extraction field, hover next to the label name in the Field annotations bar on the right, and select Manage fields. If you set up a general field, hover next to General fields and manage your fields there.
Select New extraction field to add a new extraction field. You can add more than one field.
Fill in the extraction Field name(s) and field type that you want to extract. You can select an existing field type or create a new one if what you’re trying to extract is not configured.
Select Save to save the extraction fields.

Note: In the annotation interface, the first field configured under a label will be displayed as the identifying field for an extraction, especially when the extraction is collapsed. To change which field is displayed, simply reorder the fields using drag-and-drop.

Setting up fields in Settings

To set up or modify both your general fields or extraction fields through the Settings page, apply the following steps:

Note: If you set up your fields from the Train tab, you are redirected to the Settings tab to configure them.

Important:

To configure fields via Train as well, follow these steps:

Go to Settings, then Taxonomy.
To create an extraction field, go to the Labels and fields tab.
On the specific label that you want to create an extraction field on, select the dropdown menu. Selecting the dropdown expands the list of all the fields on a given label.
To add a new extraction field, select Extraction field at the bottom.
Fill out the Field name, as well as the Extraction field type to configure your new extraction field.
To create a new general field, go to the General fields tab. Select New field in the top right corner.
Fill out the Field name and General field type to configure your new General field(s).

Setting up field types

When you set up your fields, you have to select the specific data type. The default types are:

Date
Exact Text
Inferred Text
Monetary Quantity
Number

Note: You can use a field type exclusively for general fields or extraction fields, which cannot be shared between them. Additionally, You can only use the Inferred text and Number field types for extraction fields.

The following table details when to use each field type:

Field Types
Data Type	General Field	Extraction Field	Description	Examples
String	X	X	Strings can include any characters, such as letters, numbers, and so on. Strings can also have input values that are explicitly present (spanful) in the message or inferred (spanless). Check out more details about spanful fields.	Organization name First name Address line
Date*	X	X	Dates come in varying unstructured formats and use the UiPath® pre-trained date field.	Start dates Expiration dates
Number	X	X	Quantities come in varying unstructured formats and use the UiPath® pre-trained quantity field to interpret numbers.	Number of items Change in %
Monetary Quantity*	X	X	Monetary quantities typically come in varying unstructured formats and use the UiPath ® pre-trained monetary quantity model.	Total premium value Fees due
Regex	X		If a specific field always needs to be extracted in a specific format, the rules can be configured with RegEx. For more details, check Building custom regex general fields.	A policy number that must always start with 3 letters, and end in 6 numbers
Template	X		Check the list of supported templates.	SEDOL BIC

Note:

Many fields may need to be normalized into a structured data format for downstream processes.

*Within the platform, monetary quantities and dates are general field types that are automatically normalized. For more details on on field normalization, check General fields formatting.

Spanful field

A spanful field is a data point that is explicitly stated in the text, such as a Trade ID, Policy Number.

Spanless field

A spanless field is a data point that might not be explicitly stated in the text but needs to be extracted from the message (i.e., can be inferred from the message). In other words, the span of text you want to extract might not necessarily be present in the message.

When setting up general fields, specify if the input value must be present in the message, or if it can be inferred from the message (i.e. – needs to be extracted exactly as-is from the text), or not.

Some examples of fields that may need to be spanless:

Values that need to be normalized, such as a date.
Values that need to be concatenated across different areas in an email.
Values that are not present anywhere in an email, but are implied through the nature of the email
Values that span across multiple paragraphs, lines, or columns, that is, that do not appear in a continuous span.

Note: Spanless fields are only available when the data type is configured as a string on an extraction field.

Creating a new field type

A field type is the initial state of your new field. If you do not have a field type to use, apply the following steps to set up a new field type. You can set up the new field type from the dropdown menu when creating a field, but also on the field type page itself.

Add the broadest field type possible, then fine-tune it to be more specific. Configure the field type as follows:

Give your field type a name. Check highlight A in the following image.

Note: The field type name is not used by the model for context the same way that field names are.
Define whether you are setting up a new field type for an extraction field, or a general field. Check highlight B in the following image.
When setting up your general fields or extraction fields, select the specific data type for the field type. Check highlight C in the following image.

Note: Depending on whether you set up a new field type or general field for an extraction, your data type that you can configure may vary. Additional configurations are also applicable, depending on the data type that you select.

Creating a new field type (Settings tab)

Note: Creating a new field type can also be done on the Field type page if needed. Doing it from Field pages pre-selects what it is defined for, and immediately assigns it to that field.

You can set up a new field type either through the Explore tab, or the Settings tab in the Train tab.

Once the data type has been configured on a field type, you cannot change it. Select the correct data type when creating a field type. If you do not select the correct data, you have to delete the field type and recreate it with the correct data type.

You can set up a new field type for both Extraction fields and General fields through the Settings tab.

To set up a new field type in the Settings tab, follow these steps:

Navigate to the Settings page.
Select the Taxonomy tab.
Select the Field types tab.
Select New field type.
Configure your new field type.

Creating a new field type in Explore

To set up a new field type, apply the following steps:

Under the Explore tab, select Annotate Fields.
Select the vertical ellipsis next to either the general fields or the extraction fields.
Select Manage fields.
Note: You can only create a new field type in the respective section of the extraction fields.
In the Manage fields section, select the field type dropdown menu.
Select New field type, and set up your field type.

Note: In the Field annotations pane, you must set up the field type that corresponds to a general field or the respective section of the extraction fields.