What is ‘Named Entity Recognition’ (NER)


Named Entity Recognition (NER), a subset of Data Science and Natural Language Processing (a branch of Artificial Intelligence), involves identifying and categorizing named entities in unstructured text, such as person names, organizations, and locations.

The huge amount of unstructured text data now accessible from both conventional and new media outlets. Named Entity recognition provides a central function for knowledge building from semi-structured and unstructured text sources.

Some of the early researchers who focused on extracting information from unstructured text acknowledged the significance of information “units,” encompassing names and numerical expressions.

In short, it is giving an entity or certain word a name, like giving a human a name.

Using NER to Classify Data

First, we need to classify certain words or numbers into categories(Named Entity)

You might receive an email with text such as “Please issue a cheque of $234 to Alex Tay“. Using NER, we can train Gleematic cognitive robot to classify ‘Cheque’ as a payment mode, $234 as the amount, and ‘Alex Tay’ as the receiver.

When we provide enough training data of many possible ways that human write, the robot can pick up the relevant data when reading incoming emails and classify them or even pull out some data fields into structured tables.

Recent rise in computing power and reductions in data storage costs means that Data Scientists and software-developers have more options to build large information, with millions of data sets and can be fed into machines for NER classification.

Such sources of expertise contribute to smart machine behavior. Not unexpectedly, Named Entity Extraction functions in the heart of many common technologies, see our demo.

Written by: Benny Tan

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