Mission
Pega NLP Essentials
3 Modules
4 Défis
2 heures 55 mins
Better understand the key features and benefits of Pega Natural Language Processing (NLP). Use Pega NLP to analyze and extract meaningful information from text by using text analytics to improve business performance and customer experience.
Text predictions use natural language processing to analyze incoming messages in conversational channels, such as email or chat. These predictions can help you in a variety of ways:
- Route emails to the right department
- Create the right case type while auto-populating relevant properties based on extracted entities
- Respond to users with relevant messages.
In this mission, you will learn how to train a text prediction to detect topics, extract entities, and identity the sentiment for incoming emails or chat messages.
Customer Decision Hub predictions
-
Module
Customer Decision Hub predictions
2 Rubriques
35 mins
-
Prediction Studio is the dedicated workspace for data scientists to control the life cycles of predictions and the predictive models that drive them...
Text analytics for email routing
-
Module
Text analytics for email routing
3 Rubriques
35 mins
-
Humans can effortlessly interpret a single tweet but are unable to parse a large volume of information efficiently. Businesses are exploring ways to...
Training a topic model to improve email routing
-
Défi
Training a topic model to improve email routing
4 Tâches
20 mins
-
U+ Bank plans to use Pega Customer Service™ to route incoming emails to the appropriate department based on the topic of the email. For several use...
Using entity extraction with chatbot channel
-
Module
Using entity extraction with chatbot channel
1 Rubrique
15 mins
-
Better understand the key features and benefits of entity extraction with chatbot channel. Use this module to learn how to enable the chatbot to...
Creating a chatbot channel
-
Défi
Creating a chatbot channel
5 Tâches
15 mins
-
U+ Air wants to use a chatbot to handle ticket cancellation requests through all relevant channels to reduce the workload of customer service...
Creating entity extraction model using Ruta script
-
Défi
Creating entity extraction model using Ruta script
6 Tâches
20 mins
-
U+ Air uses a chatbot to serve its customers. Currently, the chatbot recognizes when a customer wants to cancel a ticket, and the system automatically...
Enhancing entity extraction with machine learning
-
Défi
Enhancing entity extraction with machine learning
5 Tâches
25 mins
-
The U+ Air chatbot channel detects U+ Air ticket numbers using a RUTA-based model. The current entity model recognizes a ticket number in the...