Creating entities for an email bot
To ensure that Pega Email Bot detects the correct information in emails, such as a location, date, or postal code, update the training data in the system by adding new entities. For example, if you want to detect the make of cars in emails, you can create the CarMake entity.At run time, your email bot can use this detected information to provide a right response to an email.
The email bot can also use an entity for other purposes. The system can automatically copy phrase from a detected entity, for example, Ford, to a property of a related business case. For more information, see Setting up entity property mapping.
In the header of Dev Studio, click the name of the application, and then click Channels and interfaces.
In the Current channel interfaces section, click the icon that represents your existing Email channel.
On the Email channel configuration page, click the Training data tab.
If you configure multiple languages for the email bot, to filter data records by a language, in the Language list, select a language.To display data records only detected in the Italian language, select Italian.
In the list of training records, select a data record.The Review training data pane displays the detected entities and the NLP analysis section displays the entity types for the training data record.
In the Review training data section, in the data record content, highlight and right-click the text that you want to map to the new entity, and then click New entity.To select a car make in the text, highlight the word Ford.
In the Create new entity window, in the Entity name field, enter a name for the entity, and then click Submit.To create an entity for a car make, enter Car Make.
To use this training record to improve the artificial intelligence algorithm of your email bot, in the Review training data section, click Mark reviewed.
Create at least 15 records in the training sample so that the system learns how to detect the right information in emails.
- Editing data records
When you enable the recording of training data and Pega Email Bot receives emails, the system saves the content of each email as a training data record. You can then edit this training data before it is added to the model, to remove irrelevant content and fix grammatical or formatting errors in the record. This ensures that your training data is of high quality, so that the text analytics model is only trained using the most relevant, correct topics, entities, and language.
- Correcting identified topics
If you want to ensure that Pega Email Bot is accurately detecting the topic and intent of the emails it receives, you can review and correct the topics in the training data records. You train the system by correcting the identified topics in each training record, and then rebuilding the text analytics model with the updated information. This improves the accuracy of the cases and responses that the email bot suggests when it detects the relevant topic.
- Correcting identified languages
To train Pega Email Bot to detect the correct language in emails, select the language for a training record, and then build a text analytics model for the system. Correcting the detected language in training data ensures that the email bot selects the right languages to perform text analysis of emails.
- Correcting existing entities
Since Pega Email Bot does not automatically know how to respond, to ensure that the system detects the right entities in emails, correct the wrongly detected entities in the training data. When the email bot learns to detect the correct topics, entities, and language in emails, the artificial intelligence algorithms provide better responses to users.
- Correcting training data in an email bot
When you want to improve the ability of Pega Email Bot to detect topics, language, and entities, you can review and correct the training data in the system. By correcting the training data and rebuilding the text analytics model, you improve the artificial intelligence of the email bot and teach it to more accurately detect the desired information in emails. The system can then suggest the right business case or email response, based on the detected information.