Table of Contents

Building machine-learning text extraction models


Only available versions of this content are shown in the dropdown

Use Pega Platform machine-learning capabilities to create text extraction models for named entity recognition.

By using models that are based on the Conditional Random Fields (CRF) algorithm, you can extract information from unstructured data and label it as belonging to a particular group. For example, if the document that you want to analyze mentions Galaxy S8, the text extraction model classifies that as Phone.

  • Preparing data for text extraction

    In the Source selection step of the text extraction model creation wizard, select the extraction type and provide the data for training and testing of your text extraction model.

  • Defining the training set and training the text extraction model

    In the Sample construction step of the text extraction model creation wizard, select the data to use to train the model and the data to use to test the model's accuracy. In the Model creation step, build the model.

  • Accessing text extraction model evaluation reports

    After you build the model, you can evaluate it by using various accuracy measures, such as F-score, precision, recall, and so on. You can view the model evaluation report in the application or you can download that report to your directory. You can also view the test results for each record.

  • Saving the text extraction model

    After the model has been created, you can export the binary file that contains the 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.

Did you find this content helpful?

Have a question? Get answers now.

Visit the Collaboration Center to ask questions, engage in discussions, share ideas, and help others.