Apart from the content of an email body, you can optionally train the text
analytics model for your Pega Email Bot™ with the information in email
attachments. As a result, you make the training process in your system more consistent
and you improve the accuracy of natural language processing (NLP) analysis.
For example, customers interact with your email bot to apply for a bank loan,
during which they attach files describing their financial credit history. You can enhance
the training the model in the system by using training data in file attachments that contain
sample credit histories. As a result, the email bot can then improve the detection of
correct entities and topics from customers in both the body of their emails and in their
file attachments.
By default, you train the text analytics model in your system by using training
data from email bodies. By enabling the training of model based on email attachments,
you can also use training data from text files, and image-based files if you are using
an external optical character recognition (OCR) component in your system.
In the navigation pane of
App Studio, click Channels.
In the Current channel interfaces section, click the icon
that represents your existing Email channel.
In the Email channel, click the Behavior tab.
In the Text Analyzer section, select the Record
training data checkbox.
Optional: To use an advanced text analyzer configuration, perform the following
steps:
In the Text Analyzer section, select the
Use advanced configuration checkbox.
Click Add text analyzer, or if the iNLP text
analyzer type is already displayed in the list, click the
Switch to edit mode icon in the iNLP
row.
In the Text analyzer configuration window, in the
Text Analyzer type list, select
iNLP.
Select the Enable model training check
box.
Click Submit.
Click Save.
Switch to Dev Studio.
In the navigation pane of Dev Studio, click
App, and then search for the
Work-Channel-Triage-Email class.
Expand the Data ModelData Transform section for the Work-Channel-Triage-Email
class, and then click pyDefault.
In the pyDefault data transform rule, in the table row for
the pyEnableAttachmentTraining target, in the
Source column, enter:
true
Optional: To increase the limit on the amount of text extracted from file attachments in
the system, in the table row for the pyExtractedTextLimit
target, in the Source column, enter the maximum number of
bytes to extract as text from file attachments.
Save your changes by performing one of the following actions:
To save the rule that belongs to a locked ruleset to an isolated sandbox
so that you can test your changes, click Private
edit.
The following figure shows the pyDefault data transform rule in
which you enable file attachment training and increase the extracted text limit:
The pyDefault data transform rule for the email bot
What to do next: Train the text analytics model for the Email channel based on training data from
both email body and email attachments. For more information, see Training the model for the Email channel.
