Defining a taxonomy for machine learning topic detection
Define the taxonomy that you want to use for topic detection. You can use an existing taxonomy, upload your own taxonomy file, or create a new taxonomy.
.xlsxfile that contains the taxonomy. For guidelines on creating a taxonomy, see Requirements and best practices for creating a taxonomy for rule-based classification analysis on Pega Community.
In the Taxonomy selection wizard step, specify the model taxonomy by performing one of the following actions:
Choices Actions Use an existing taxonomy
Click Select taxonomy.
From the drop-down list, select a taxonomy.
Upload your own taxonomy
Click Upload file.
Click Choose file, and then select the taxonomy file.
Create a new taxonomy
Define a topic hierarchy and corresponding keywords.For further instructions, see corresponding steps in Creating a taxonomy for keyword-based topic detection.
- Creating machine-learning topic detection models
Efficiently connect your customers with the right consultant by providing training data to a topic detection model.
- Detecting the topics of text fragments
Efficiently categorize and rout customer inquiries to the corresponding customer service consultant with topic detection. Topic detection scans a piece of text and determines the underlying topic, and then automatically assigns the text to a predefined category.
- Analyzing natural language
Effortlessly analyze and extract meaningful information from large volumes of text with the use of text analytics. Based on your findings, you can further improve business performance and customer experience.
- Text analytics accuracy measures
Models predict an outcome, which might or might not match the actual outcome. The following measures are used to examine the performance of text analytics models. When you create a sentiment or classification model, you can analyze the results by using the performance measures that are described below.