The huge amount of unstructured text data now accessible from both conventional and new media outlets, including social media, offers a rich source of information if the data can be organized. Named Entity Extraction provides a central function for knowledge building from half-structured and unstructured text sources.
The value of information “units” including names (for example personal names, associations, place names) and numerical expressions (as time, date, cash and percent of terms) is recognized by some of the first researchers who worked on the extraction of information from unstructured text. In 1996, they coined for themselves the word “called object.”
What is a Named Entity? Named-entity recognition (NER) (also known as entity identification, entity chunking and entity extraction) is a subtask for information extraction that seeks to locate and classify the named entity specified in unstructured text into predefined categories such as individual names, organizations, places, medical codes, time expressions, amounts, monetary values, percentages, etc.
In short, it is giving an entity or certain word a name, like giving a human a name.
First, we need to classify certain words or numbers into categories(Named Entity) such as the below example.
E.g. Please issue a cheque of $234 to Alex Tay.
‘Cheque’ as a payment mode, $234 as the amount, ‘Alex Tay’ as the receiver.
When we provide enough training data of every possible way that human will write this to train the robot, the robot will be able to pick up the relevant data when reading incoming email and put into a structured format for processing.
Data scientists and developers will build large information bases with millions of individuals and hundreds of thousands of details in view of recent rises in computing power and reductions in data storage costs. 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
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