Named Entity Recognition

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Named Entity Recognition (NER) is also known as “Entity Extraction”. It is a Natural Language Processing (NLP) technique that seeks to locate and classify named entities mentioned in any form of unstructured text.

Each word is identified in predefined categories like Organization, Place, Person, Time Expressions, Quantities, Monetary Values, Percentages, etc.

Extraction of named entities from unstructured contextual data is beneficial for analyzing different types of textual data.

With tremendous advancements in NLP, machines are getting smarter. They can now intelligently understand large volumes of textual data that result in numerous use-cases like machine translation, text summarization, etc.

In this blog, we will discuss:

1. What is Named Entity Recognition (NER)?
2. How does NER work?
3. What are the applications of NER?
4. How can you use NER?

1. What is Named Entity Recognition (NER)?

Named Entity Recognition is a sub-task of information extraction. It is a widespread technique to identify and segment the named entities from text documents and has proven tech for initial text classification.

With the help of Named Entity Extraction, machines can understand what a piece of text contains. NER can be used to analyze vast volumes of unstructured data, such as emails, Twitter feeds, comments, queries, etc. Also, using such technology helps to attain information about the text quickly.

For example, let us consider the sentence below:

Michael Bran was born on 30 July 1943.

Michael Bran could be considered a ‘person entity’ belonging to the person category, whereas 30 July 1943 could be viewed as a ‘date entity’ belonging to the date category.

2. How does NER work?

Textrics uses an efficient Named Entity Recognition technology, where extracting named entities from articles' contextual data is faster.

In most cases, the NER model is a two-step process:

1. Detection/Recognition of Entities - This step involves detecting a word or string of words that form an entity. For example, “Andaman and Nicobar Islands” is a single entity made up of four words or tokens.
2. Categorization - In this step, entity categories need to be created first, such as Name, Location, Event, Organisation, etc. Any kind of entity categories can be created that caters to your needs. You can also provide granular rules for which entities belong to which categories in instances of ambiguity or task-specific ontologies.

Machine learning helps machines learn and improve over time. Through these steps, we can feed the NER model with relevant training data. Eventually, you’ll be able to teach the model how to detect entities themselves.

Further Reading → Text Tagging

3. What are the applications of NER?

NER is used in many fields of Natural Language Processing (NLP). It helps you to quickly identify the critical elements in a text for large databases. You can also glance and understand the subject or theme of a text body and quickly group texts based on their relevancy or similarity.

A few typical applications of Named Entity Extraction are:

1. Customer Support - Categorizing tickets that contain user requests, complaints and questions become easy and quick. You can filter by priority keywords and improve response times.
2. Processing of Resumes/HR - Resumes are a classic example of unstructured text as each resume is often organized and formatted differently. Recruiters can use NER to categorize and instantly extract the most relevant information about candidates.
3. Health Care - With the help of Named Entity Extraction, hospital administrators can improve patient care standards and reduce workloads by extracting information from any kind of lab report.
4. Insights from customer feedback - Named Entity Recognition can help you organize customer feedback such as comments, reviews, etc., and pinpoint recurring problems. It is crucial for improving those aspects of your business where customers are facing problems.
5. Education - Using NER systems in schools, universities, and colleges enables students and researchers to find relevant material faster by summarizing papers and archive material.

4. How can you use NER?

As we observed in the previous sections, any business or project can benefit from NER. Data is everywhere nowadays, and using automated systems can simplify the processes of data analysis.

You can quickly answer many real-world questions, such as:

• Which companies were mentioned in a news article?
• Were specific products mentioned in complaints or reviews?
• Does a particular tweet contain the name of a person, or his/her location, etc.?

With Textrics, you can solve huge data structuring problems using Named Entity Extraction in a matter of a few seconds.

Are you ready to see how it works?

Start right away! Contact our team or request a free demo now.

Further Reading → Topic Modeling