Named entities are mostly chunks of text and not simply singular words. Hence, some chunking or parsing prediction model is needed to predict whether a group of tokens are from the same entity or not. NER needs the following requirements for completing its process:
Chunking and text representation
Inference and ambiguity resolution algorithms
Modeling of Non-Local dependencies
Implementation of external knowledge resources and gazetteers
Named Entity Recognition is a process in which an algorithm takes a string of text as input and finds out the relevant nouns (people, places or organizations) that are present in that string. The various areas where it plays a major role are mentioned below:
Online content is generated on a daily basis in various news and publishing houses. These need to be managed correctly in order to get the best result out of them. NER can scan the entire article easily and reveal which are the major people, organizations, and places discussed in them.
Suppose, an internal search algorithm is being designed for an online publisher having millions of articles. If we try to search all the words in millions of articles, it will take a lot of time. Instead, if NER can be run once on all the articles, then this will simply speed up the search process in a great manner
Automating the recommendation process is one of the major use cases of Named Entity Recognition. The recommendation system’s job is to dominate how we discover new content and ideas in today’s world.
To make the process of customer feedback handling smooth, NER plays an important role. For example, if the customer support department of a hardware store is being handled that has multiple branches worldwide, then we need to go through a number of mentions in the customers’ feedback
On the other hand, if it is passed through the NER API, it will automatically recognize the essential entities on its own.
If we talk about an online journal or publication site, then we can see that it holds millions of research papers and scholarly articles. There can be hundreds of papers written on a single topic with slight modifications. Organizing all this data is the job of NER. The extensive amount of data coming from social media, email, blogs, news and academic articles is extracted, and categorized with the help of NER.