Mission
Pega NLP Essentials
3 Module
4 Challenges
2 Std. 25 Min.
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 identify the sentiment for incoming emails or chat messages.
In der folgenden Mission verfügbar:
Pega NLP overview
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Modul
Pega NLP overview
2 Themen
15 Min.
-
Gain a greater understanding of the key features, capabilities, and benefits of Pega natural language processing (NLP) in a decision management...
Text analytics for email routing
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Modul
Text analytics for email routing
2 Themen
25 Min.
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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
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Challenge
Training a topic model to improve email routing
5 Aufgaben
20 Min.
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U+ Bank plans to use text predictions to route incoming emails to the appropriate department based on the topic of the email. For several use cases...
Using entity extraction with chatbot channel
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Modul
Using entity extraction with chatbot channel
1 Thema
15 Min.
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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
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Challenge
Creating a chatbot channel
5 Aufgaben
15 Min.
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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
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Challenge
Creating entity extraction model using Ruta script
6 Aufgaben
20 Min.
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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
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Challenge
Enhancing entity extraction with machine learning
5 Aufgaben
25 Min.
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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...
