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

Last updated Aug 1, 2025

Generating your extractions

Prerequisites

Note: Choose a label that has no performance indicators or warnings and is at an appropriate precision or recall level for your use case.

The Extraction validation process is required to understand the performance of these extractions through Validation.

Decide on the extraction that you want to train. Report > Statement of Accounts is an example of a schema to train.

To automate this process, extract the following data points to input into a downstream system:​



Note: This is only applicable if you are training in Explore. In Train, selecting into an extraction training batch pre-loads the extractions​.

Use this training mode as required, to boost the number of training examples for each extraction, that is, a set of fields assigned to a label, to at least 25. This allows the model to accurately estimate the performance of the extraction.

Important: If you use the Preview LLM, we recommended to stop annotating more examples once you reach 25 examples for each field. This is sufficient for in-context learning and validation, and more examples do not improve performance.

Steps

To generate your extractions, proceed as follows:

  1. Navigate to the Explore tab.
  2. Select Label, and then select the label you want to generate extractions on.


  3. Select Predict extractions, which generates extractions on a per page basis in Explore. This means that it applies predictions on all the comments on a given page.​

    Note: Each time you go to the next page, you need to select Predict extractions again.​

    In addition, you can generate extractions on an individual comment level by selecting Annotate Fields, and then Predict extractions​. For more details, check Predicting extractions.



  4. After making the extraction predictions, if the model picked up field extractions on the comment, it highlights the relevant span in the text. The model displays the extracted value in the side panel. To learn how to validate the predicted values, check Validating and annotating generated extractions.



Predicting extractions

This section describes what happens when you predict extractions:

  • The model uses generative models and maps each of the data points that you previously defined in our extraction schema, to relate to them to an intent, that is, a label​.
  • It extracts and returns them in a structured schema, for an SME to go through and confirm.
  • The structured schema is intended to enable more complex automations, and is structured in JSON format in the API for consumption by any downstream automations.​

  • Prerequisites
  • Steps
  • Predicting extractions

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