Introduction to Named Entity recognition

Named Entity Recognition is a very useful process of Natural Language Processing(NLP) which generally deals with identifying and classifying named entities. The unstructured text is taken and classified into various named entities categories like organizations, persons, places, time, etc. So basically, named entities are properly identified and segmented into many classes.

Named Entity Recognition systems are generally developed with various statistical and machine learning methods. NER has many applications for business purposes.

It is basically a sub-task of data extraction that looks to locate and order named entities referenced in unstructured text into some predefined classes, for example, organizations, person names, percentages, quantities, monetary values, locations, etc.

How do Named Entity Recognition works?

While reading the text, we intuitively identify items such as values, individuals, locations, and so on. For example, in the sentence "Elon Musk co-founded and leads Tesla, SpaceX, Neuralink and The Boring Company

• Entity 1 - Person: Elon Musk
• Entity 2- Company: Tesla, SpaceX, Neuralink, Boring Company
   

However, we must first teach computers in recognizing entities so that they can categorize them. Machine learning and Natural Language Processing (NLP) play a very important part in doing so.

Machine learning and Deep learning help machines to learn, develop and improve over time, while Natural language processing properly analyses the structure and norms of language and creates intelligent systems that are capable of deriving the meaning from speech and text. NER is a platform with which we will be able to detect a word or group of words that comprise an entity (e.g. Apple) and know which entity group it belongs to in order to learn what it is.

Hence, to begin with, we must establish entity categories such as Name, Company, locations, and so on, and then feed proper training data to a NER model. After that, you may teach your model to detect entities by tagging some sample words coupled with their matching entities.

Implementation:

## Installation of Spacy library

!pip install spacy==3.0.6

## Initializing named entity recognition function

nlp = en_core_web_sm.load()

## Input text:

raw_text="""Elon Reeve Musk FRS is an entrepreneur and business magnate. He is the founder, CEO, and Chief Engineer at SpaceX; early stage investor, CEO, and Product Architect of Tesla, Inc.; founder of The Boring Company; and co-founder of Neuralink and OpenAI.He lives in United States."""

## Passing input text into the named entity recognition model

text1= nlp(raw_text)
spacy.displacy.render(text1,style="ent",jupyter=True)

## Output

NER using Spacy

Applications:

Some of the prominent applications of Named Entity Recognition are as follows:

1. Named Entity Recognition can be used in recommendations based on a person’s search history and on a variety of content sources, including movies, blogs, etc.
2. Named entity recognition also helps various recruitment teams in quickly extracting the most important and relevant details about the candidates from their resumes, such as professional and personal information.
3. Named Entity Recognition has the ability to scan the entire articles automatically for the identification of key organizations, and places mentioned, making it simple to define news content.

Conclusion

Named Entity Recognition is one of the best ways for extracting information from unstructured data. The best way to start with NER is to use entity extraction APIs using a sophisticated text mining platform.