Automatically find the names of people, places, and organizations in text across many languages.

Accurate & adaptable statistical entity extraction

Rosette® Entity Extractor (REX)  delivers structure, clarity, and insight, by revealing the key information—names, places, organizations, products, and other words and phrases—lying hidden within large volumes of unstructured Big Text.

REX is the foundation for applications in eDiscovery, social media analysis, financial compliance, and government intelligence. The effectiveness of these mission-critical applications depend on REX for its accuracy, robustness, and ability to find entities across many languages.

By nature, statistically trained models are most accurate on the type of data they are trained on. Besides machine learning from a wide range of text beyond news articles, REX is unique among named entity recognition software in its adaptability. REX’s field training mechanism enables you to add your text data to your entity extraction model to increase REX’s accuracy on your text.

Text Analytics


  • Component of the Rosette SDK
  • Simple API
  • Fast and scalable
  • Industrial-strength support
  • Easy installation
  • Flexible and customizable
  • Java or C++
  • Unix, Linux, Mac, Windows

How It Works

Statistical Entity Extraction

REX Machine Learning

Statistical modeling with advanced linguistics solves three major problems:

  1. Finds entities which cannot be exhaustively listed.
  2. Finds entities which are yet unknown.
  3. Considers context so that place names (Newton, MA) are not confused with people names (Isaac Newton).

Because of these problems, entity extraction for people, organizations, products, and locations can only be accomplished with a statistical model that is trained on millions of news and blog articles and has learned the context within which one finds these entities.

Customized Entity Extraction

Field Training for Increased Accuracy

For users with text that is particularly challenging in format, style, or vocabulary, REX’s unique field training capability has multiple mechanisms to adapt its statistical model to their data. Users just add a quantity of their data (unannotated or annotated), and rebuild the model for maximum accuracy.

Pattern-Matching Rules

REX Rules

Rules expressed as regular expressions find entities which follow a pattern, such as dates, times, and email addresses. Many standard string patterns are included with REX; customers can customize by editing or adding their own rules, based on their specific needs.

Custom Entity Lists

REX Lists

Custom lists are helpful when users know that specific words or phrases in their data are almost never misspelled and always refer to the same thing (i.e., are unambiguous). An example is a list of basic colors like red, yellow, and green for tweets mentioning a clothing manufacturer. REX comes with such lists for entity types like religions and nationalities. For identifying specific entities that can have variant names or might be ambiguous like the various Presidents Bush, REX should be combined with the Rosette Entity Resolver (RES).

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  • Arabic
  • Chinese, Simplified
  • Chinese, Traditional
  • Dutch
  • English
  • French
  • German
  • Hebrew
  • Italian
  • Indonesian
  • Japanese
  • Korean
  • Pashto
  • Persian
  • Portuguese
  • Russian
  • Spanish
  • Urdu
  • Person
  • Location
  • Organization
  • Product
  • Title
  • Nationality
  • Religion
  • Credit Card Number
  • Geographic Coordinate
  • Money
  • Generic Number
  • Personal ID Number
  • Phone Number
  • Email Address/URL
  • Distance
  • Date
  • Time
Code Base
Platform Support
Red Hat

REX in Action


REX Demonstation Video

Entity Extraction Plus

Entity extraction is often used in combination with other text analytics to solve a specific problem.

Select Customers

Contact us for more information about integrating REX
into your application.

Learn More

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Download the Rosette Entity Extractor Datasheet

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Whitepaper: Entity Extraction Enables Discovery

Allows the searcher to find relevant information even when they don't know what they're looking for.

Startup Program

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