About XTractor

Computers are powerful and intelligent tools

Information is the currency of the modern world. Modern computers can process and analyze millions or billions of individual pieces of information every hour. They are the ultimate power in processing information. They can find the information you need, they can link new and existing information, and they can alert you when something unexpected is about to happen.

But they can't understand human language

Modern computers can be powerful intelligent information analysis tools, but only when the information is normalized and encoded in a mathematically acceptable form: for example, in a database record.

Unfortunately, this rules out the major source of information that we humans use to communicate -- Free Text. That's right, the all powerful computer is helpless when is asked to extract information from an email, word document, web page, etc. Why? Because the same information can be represented in many different ways in plain text. For example, how will the computer know whether "White" is a person or a color? How will it guess that "Infogistics" is a company and that "Small Hopes" is a town? Indeed how?

XTractor will solve this " communication problem"

XTractor by Infogistics is a successor to the Named-Entity Recognition System developed by our key scientists at the University of Edinburgh which won the prestigious MUC-7 competition organized by the American Defence Research Agency (DARPA) in 1998. XTractor is an engine which sifts through large volumes of texts and creates database records for the objects which are mentioned in the text, such as people, organizations, locations, vehicles, etc. Moreover, it normalizes and unifies information, so for example, if somebody is referred to as "John D. Smith", "John", "J. Smith", or even "he", XTractor can figure out that this is the same person. And most importantly, XTractor identifies the key relationships or links between the objects that it finds.

For example

For example, from a sentence "White is a managing director of Cyber Corp." it will identify that there is a person with surname "White", there is a company called "Cyber Corp." and it will create an "employment" link between these two objects. It will also record particular details of the employment: "managing director".

XTractor can be linked to a document Viewer where the objects XTractor finds in are highlighted, and information aggregated from many different places of a document can be seen at a glance. For instance, at the beginning of a document XTractor identifies that John Smith is 42 year old, later on it appends that he works at Good Co. Ltd., at the end of this document it identifies that he drives a red Nisan. XTractor makes sure that all this information is kept under the single record "John Smith", suitable for further processing by the computer, or for displaying to humans.

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