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Defining a taxonomy for machine learning topic detection

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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.

If you want to upload your own taxonomy, prepare a .csv, .xls, or .xlsx file 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.
  1. In the Taxonomy selection wizard step, specify the model taxonomy by performing one of the following actions:

    Choices Actions
    Use an existing taxonomy
    1. Click Select taxonomy.

    2. From the drop-down list, select a taxonomy.

    Upload your own taxonomy
    1. Click Upload file.

    2. Click Choose file, and then select the taxonomy file.

    Create a new taxonomy
    1. Click Create.

    2. Define a topic hierarchy and corresponding keywords.

      For further instructions, see corresponding steps in Creating a taxonomy for keyword-based topic detection.
  2. Click Next.

Upload sample records to train the model and to test whether the model assigns the topics correctly. For more information, see Uploading data for training and testing of the topic detection model.

  • 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.

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