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.