Name, Place, Animal, Thing…How Nouns in Sentences Benefit Businesses?

machine-learning-named-entity-recognition
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Omkar Khapare

Machine learning has its branches spread out in multiple domains. 

Today, we are going to learn more about how it helps us gain qualitative insight into sentences.

Practicing Named Entity Recognition in Python or any other respective language is a sophisticated process of extracting entities from a piece of text and classifying them as ‘individuals’, ‘place’, ‘date’, ‘company’, ‘product terms’, etc.

It collects quality semantic data from a piece of content and helps any user promptly understand the subject of any given text.

What is Named Entity Recognition?

Simply put, it is a process where an algorithm takes a string as input and identifies relevant nouns mentioned in that string.

Let’s take a look at a few real-life use cases.

3 Real Life Use Cases of Named Entity Recognition

1 - Classifying Content for Media Providers 

News and publishing houses work on a large amount of data on a daily basis and managing such datasets is very important to get the most out of each article. 

Named Entity Recognition automatically scans the entire article to reveal which major places, organizations, people or things are mentioned in them. 
 

entity-recognition

Credit: towardsdatascience.com

2 - Providing Content Recommendations 

One of the important applications of Named Entity Recognition is automating the recommendation process. 

Recommendation systems control how we discover new ideas and content in today’s world.

netflix

For example, Netflix is blooming because of their engagement and event addictive interface that never lets a user run out of media.

It always provides a quality recommendation based on data extracted from Named Entity Recognition engines.

3 - News Publishers

News publishers use Named Entity Recognition to recommend similar articles to users.

The below example shows how BBC News recommends similar articles that form a cluster of related news to better serve its users.

First, the entities from a particular article are extracted and those entities are matched with entities of other articles. 

If they match or lie within the semantic range of those chosen entities, then those articles are grouped together.
 

news

Credit: towardsdatascience.com

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Stay tuned for more on Machine Learning and its applications in our next few blogs.

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