ミッション
Pega NLP Essentials
3 モジュール
4 チャレンジ
2 時間 25 分
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.
このモジュールは、下記のミッションにも含まれています。
Pega NLP overview
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モジュール
Pega NLP overview
2 トピック
15 分
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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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モジュール
Text analytics for email routing
2 トピック
25 分
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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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チャレンジ
Training a topic model to improve email routing
5 タスク
20 分
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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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モジュール
Using entity extraction with chatbot channel
1 トピック
15 分
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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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チャレンジ
Creating a chatbot channel
5 タスク
15 分
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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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チャレンジ
Creating entity extraction model using Ruta script
6 タスク
20 分
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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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チャレンジ
Enhancing entity extraction with machine learning
5 タスク
25 分
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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...
