Building machine learning text extraction models
Use Pega Platform machine learning capabilities to create text extraction models for named entity recognition.
- Define an entity model in which to accommodate the entities trained as a result of machine learning. For more information, see Creating entity models.
- Ensure that the system locale language settings are set to UTF-8.
- Specify a repository for text analytics models. For more information, see Specifying a database for Prediction Studio records.
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.
- Best practices for creating extraction models
Use extraction analysis to detect and classify named entities into predefined categories, for example, names of people, locations, organizations, and so on.