Skip to main content

         This documentation site is for previous versions. Visit our new documentation site for current releases.      

Natural language processing reference

Updated on July 5, 2022

Review the following articles for best practices for natural language processing (NLP) in , including such operations as configuring and training text analytics models, using the NLP Sample application and the text analytics APIs.

  • NLP outcomes database tables

    Obtain information about the outcomes of natural language processing (NLP) by running reports on the database tables in which Pega Platform stores the results of text analysis. You can analyze the NLP outcomes in business intelligence (BI) tools to gain insights into your interactions with customers, or use them for auditing purposes to meet contractual requirements.

  • Machine-learning models for text analytics

    You can use Pega Platform to analyze unstructured text that is contained in different channels such as emails, social networks, chats, and so on. You can structure and classify the analyzed data to derive useful business information to help you retain and grow your customer base.

  • Creating entity extraction rules for text analytics

    You can use the default decision data that contains entity extraction rules in Pega Platform to create custom rules for extracting entities from text.

  • Providing feedback to text analytics models

    Increase model accuracy by correcting the sentiment value, intent, category, or entity classification of the analyzed text. After collecting the corrected records, retrain the corresponding model to classify similar records more accurately in future analyses.

  • Definition class of text analytics Decision Data rules

    Use the following automaticallygenerated frameworks for Decision Data rules to quickly create or update your text analytics resources, such as models and lexicons.

  • Intelligent interaction in text analytics

    Use the pxRunInteraction activity provided by Pega Platform for parsing and analyzing text data (emails, tweets, posts, instant messages, and so on) through conversational user channels.

  • Feedback loop for text analysis

    In Pega Platform, you can improve the accuracy of text analytics models in your application by manually correcting unexpected or inaccurate text analysis results.

  • Training data size considerations for building text analytics models

    Build your text analytics models efficiently by choosing an optimal algorithm for your training data size. Consider the building times and prediction accuracies that different types of algorithms available in Pega Platform can provide.

  • Learning natural language processing with NLP Sample

    NLP Sample is a reference application that contains a set of ready-to-use tools and example use cases to guide you through natural language processing (NLP) on Pega Platform. After you become comfortable with the examples, you can build your own tools for text analytics and use NLP Sample to analyze news-feeds, emails, and posts on social media.

  • Text Analytics APIs

    The Text Analytics APIs directly separate the Intelligent Virtual Assistant (IVA) and Text Analyzer rules into independent modules. The introduction of modularity between these components provides more reliability by preventing potential IVA integration issues each time a text analyzer configuration changes.

Have a question? Get answers now.

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

Did you find this content helpful?

Want to help us improve this content?

We'd prefer it if you saw us at our best. is not optimized for Internet Explorer. For the optimal experience, please use:

Close Deprecation Notice
Contact us