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Recognizing user intent


Create intent analysis models to enable your application to detect the ideas that users express through written communication. For example, you can use an intent model when you want your chatbot to understand and respond when someone asks for help.

  • Setting up an intent detection model

    Start the build process of an intent detection model in Prediction Studio by selecting the model type and the language of the model that you want to build.

  • Preparing data for intent detection

    In the Lexicon selection step, provide a sentiment lexicon and a list of intent types, together with words or phrases that are specific to each intent type that yo want to detect.

  • Uploading data for training and testing of the intent detection model

    In the Source selection step, select and upload the file that contains training and testing data that is required to create a model.

  • Defining training and testing samples, and building the intent detection model

    In the Sample construction step, determine which data to use to train the model and which data to use to test the model's accuracy.

  • Accessing intent analysis model evaluation reports

    After you build the model, you can evaluate it by using various accuracy measures, such as F-score, precision, and recall. You can also view the test results for each record.

  • Saving the intent detection model

    After the model has been created, you can export the binary file that contains that model to your directory and store it for future use. You can also create a specialized rule that contains the model. That rule can be used in text analyzers in Pega Platform.

  • 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.

  • Testing text analytics models

    You can perform ad-hoc testing of text analytics models that you created and analyze their performance in real-time, on real-life data.

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