Skip to main content
LinkedIn
Copied!

Table of Contents

Configuring email signature parsing for contacts

Version:

Only available versions of this content are shown in the dropdown

Pega Sales Automation uses natural language processing (NLP) on a contact's email signature to create default contact values.

For example, NLP can extract the first and last name, title, and phone number from a contact in Outlook add-in based on the email signature and add these values to a Pega Sales Automation contact.

After you configure the integration between Microsoft Outlook and Pega Sales Automation, configure signature parsing to use NLP to fill out contact information. Perform all the procedures in this section.

  • "Configuring signature parsing"
  • "Configuring signature parsing after upgrading to current release"
  • "Further customizing the shipped models"

Configuring signature parsing

You must have administrator access to perform these steps.
  1. In the header of Dev Studio, click Your application name Channels and interfaces .

  2. Optional:

    If you have not created an Email channel as part of the Intelligent Virtual Assistant configuration, create an Email channel.

  3. Open the Email channel and perform the following steps:

    1. On the Behavior tab, in the Text Analyzer section, select the Record training data check box.

    2. Click Save.

    3. In the Text Analyzer section, click Open text analyzer rule.

    4. In the Text extraction section, verify that the pySystemEntities and SASystemEntities models are on the list.

    5. Optional:

      If these models are not listed, add them by clicking Add extraction model.

    6. Click Save.

Configuring signature parsing after upgrading to current release

You must have administrator access to perform these steps.
  1. In the header of Dev Studio, click Your application name Channels and interfaces .

  2. Open an existing email channel that you want to associate with signature parsing.

  3. On the Behavior tab, in the Text Analyzer section, select the Record training data and Use advanced configuration check boxes.

  4. In the Text Analyzer section, click the Switch to edit more icon on the right side of the iNLP text analyzer.

    1. In the Text Analyzer type field, select iNLP.

    2. Select the Enable model training check box.

    3. Click Submit.

  5. In the Text Analyzer section, delete the existing pyNER model by clicking the trash icon on the right side of this extraction model.

  6. Add the pySystemEntities and SASystemEntities models by clicking Add text analyzer.

  7. Click Save.

Further customizing the shipped models

You can further customize the pySystemEntities and SASystemEntities models to fit your business needs.

This task is performed by developers.
  1. In the navigation pane of Dev Studio, click Records Decision Decision Table .

  2. Search for and open the pyAllowedNLPShippableRulesets decision table.

  3. Save the pyAllowedNLPShippableRulesets decision table into your implementation layer.

  4. In the table, perform the following steps:

    1. Add your rulesets under the Ruleset column.

    2. Set the return value under the Actions column to true.

    3. Click Save.

  5. In the navigation pane of Dev Studio, click Records Decision Decision Data .

    1. Search for and open the SASystemEntities data model.

    2. On the Data tab, in the Entity analysis section, download the training data in every language by clicking Download.

  6. Create a binary file for each model for each language that you want to use, by clicking Create Technical Binary File .

    1. In the Label field, enter NLPDefault.

    2. In the App Name (Directory) field, enter information in the following format: Rule purpose>_<language>, for example, pyNER_English.

    3. In the File Type (extension) field, enter zip.

    4. Save the file in the implementation ruleset.

  7. Open the binary file and upload the training data .zip file for each language by clicking Upload file.

    The extraction of entities depends on the training data that you feed to the model. The pySystemEntities model is a Pega Platform model, whereas the SASystemEntities model is a Pega Sales Automation model.
Did you find this content helpful?

Have a question? Get answers now.

Visit the Collaboration Center to ask questions, engage in discussions, share ideas, and help others.

Ready to crush complexity?

Experience the benefits of Pega Community when you log in.

We'd prefer it if you saw us at our best.

Pega Community has detected you are using a browser which may prevent you from experiencing the site as intended. To improve your experience, please update your browser.

Close Deprecation Notice
Contact us