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What Is Named Entity Recognition?
Here the blocks are the entities identified by NER.
It can extract key information quickly from the available set of large data.
For example, healthcare Institutions use NER to extract essential medical data from patient records.
Many companies use it to identify whether they are mentioned in any publications.
Key Concepts: NER
It is important to know the basic concepts involved in NER.
Lets discuss some key terms related to NER to be familiar with.
Here are some mathematical techniques:
How Does NER Work?
Named Entity Recognition (NER) operates as an extraction of information.
It generally involves tasks like tokenization.
Here, the text at first split into tokens before NER started identifying entities.
#2.Identify Entities
Potential named entities can be detected by using statistical methods or linguistic rules.
The common categories are organization, date, location, person, and more.
This is achieved by machine learning models that are trained on labeled data.
For example, Bill Gates would be recognized as a person and Microsoft as an organization.
#4.Contextual Analysis
NER never stops at recognizing and classifying entities.
It often considers the context to enhance accuracy.
This step considers the context where the entities appear, giving accurate categorization.
For example, Bill Gates Founded Microsoft.
#5.Post-Processing
After initial identification and categorization, post-processing is necessary to refine the final results.
This involves resolving ambiguities, using knowledge bases, merging multi-token entities, and more to improve entity data.
This approach utilizes algorithms, including maximum entropy and conditional random fields, to get complex statistical language models.
#2.Rules-Based Systems
This method utilizes different rules to gather information.
It includes titles or capitalizations, such as Er.
This method might miss the textual variations which are not included in the training annotations.
Thats why rules-based systems are unable to deal with complexity and machine learning models.
This method faces trouble in categorizing named entities that have various variations in spellings.
Also, there are many other emerging NER methods.
The unsupervised learning models are more capable of executing complex jobs than supervised models.
This method minimizes human interaction.
#7.Statistical Systems
This method uses probabilistic models that are trained on textual relationships and patterns.
It helps predict named entities easily from new text-based data.
Benefits of NER
NER models provide numerous benefits.
Since NER is evolving, research and development teams must refine various techniques to tackle these challenges.
So, managing them efficiently is crucial to get the most out of an article or news.
For each search query, your internal search algorithm ends up gathering all the words from those articles.
This is a time-consuming process.
This will speed up your search process.
#3.Content Recommendations
Automating the recommendation process is a major use case of NER.
Recommendation systems guide in discovery of new ideas and content.
Netflix is the best example of this.
Its proof that building an efficient recommendation system helps you become more event addictive and engaging.
For news publishers, NER works effectively in recommending similar articles.
This can be done by gathering tags from a specific article and recommending other content that has similar entities.
#4.Customer Support
For every organization, customer support is a major thing.
Thats why there are multiple ways to make the function of customer feedback handling smooth.
NER is one of them.
Lets understand this with an example.
Here, NER pulls out the tags San Diego (location) and sport shoes (product).
you could develop a database consisting of feedback that is categorized into various departments and analyze each feedback.
#5.Research Papers
An online publication or journal website holds plenty of scholarly articles and research papers.
you’ve got the option to find hundreds of papers resembling similar topics with slight modifications.
So, organizing all this data in a structured manner can be a complicated task.
To skip the long process, you could segregate these papers based on the relevant tags.
For example, there are thousands of papers on machine learning.
This will help you find the article quickly as per your requirements.
It involves various models and processes and brings many benefits to professionals and businesses.
Its also used for various applications apart from NLP.
You may also explore some Best NLP Courses to learn natural language processing
