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What Is Named Entity Recognition?

Named Entity Recognition (NER) is a powerful tool in natural language processing that identifies and classifies key elements in text, such as names of people, organizations, locations, and more. By extracting this information, NER enhances data analysis and understanding. How might this technology transform the way we interact with the vast sea of digital information? Join us to uncover its potential.
T.S. Adams
T.S. Adams

Although from an end-user's perspective the process of inputting data into a computer is fairly simple, the process actually initiates several other smaller processes. For each piece of data, the computer must translate that information into a language it understands. Computers use an internal language known as binary in which a collection of "1" and "0" characters form together to tell the computer what to do. Named entity recognition is a method of streamlining the translation process from input to data, assisting the computer in breaking sentences into their component parts.

When you enter data using most computer programs or webpages, the program or webpage generally asks you to input a specific piece of information into each field, such as the "Name" or "Address" fields. A named entity recognition program does away with most of that, allowing the end-user to input strings — sentences — of text instead, providing a more natural interface. The program takes the data you entered and sorts it into pieces that it can more readily understand.

Woman doing a handstand with a computer
Woman doing a handstand with a computer

Take a sentence such as "Joe ordered four bushels of bananas." In a traditional program, the end-user would have to input "Joe" in the "Employee Name" field, "Bananas" in the "Purchase Type" field, "four" in the "Quantity" field, and "bushels" in the "Units" field. In a named entity recognition setup, the end-user would simply enter the entire sentence as written. As soon as the user clicks submit, the computer sorts the sentence into pieces, performing the same breakdown of data that the user would formerly have used.

Although this is far more streamlined than a traditional input program designed from fields, there is one substantial drawback: potential failure of the translation algorithm. Less intelligent named entity recognition algorithms might only "understand" sentence in one specific way. In other words, if the employee rephrased the sentence and wrote "Four bushels of bananas were ordered by Joe," the program might have difficulty understanding and sorting the parts. For this reason, even though the end-user is no longer responsible for entering data into fields, it is still not as freeform as the system might seem at first.

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