Named entity recognition (NER) is a natural language processing technique (NLP) that recognizes and extracts essential information from the text. NER detects and categorizes necessary information in the text known as named entities.

The very purpose of Named Entity Recognition is to extract structured information from unstructured text. It helps machines identify and organize entities helpfully. 

When we read text, we identify named entities such as people, organizations, locations, and so on. Consider the example below:

Anand Mahindra is the chairman of Mahindra & Mahindra Limited, a major player in the Indian automotive manufacturing industry.

Therefore, in the above sentence, we recognize three types of entities.

Anand Mahindra is labeled as PERSON, which indicates an entity representing the name of a person.

Mahindra & Mahindra is tagged as ORG, which represents Organization. It implies that it is identified as an entity that refers to businesses, companies and so on.

India is classified as GPE, which represents the Geopolitical entity. GPE indicates countries, states, cities, and places.

The major applications of NER are in the areas of artificial intelligence, such as machine learning, neural networks, and deep learning.

Let’s discuss the basics, the various approaches in Named Entity Recognition, and how to implement the Named Entity Recognition model.

What are the Applications of Named Entity Recognition?

NER has an array of applications in Natural language processing and information extraction. Below are a few examples.

Optimizes Search Engine Algorithms

It is difficult to search for a query against several websites while designing a search engine algorithm. Hence, one of the easiest ways is to run a Named Entity Recognition model on the articles and permanently store the entities related to them.

Also, for a fast and effective search, it is easy to compare the key tags in the search query with those tags.

Classifies Content 

News agencies generate a large amount of online content on an everyday basis. Indeed, handling them becomes demanding. However, using Named Entity Recognition, you can scan all of the articles to help identify and extract the essential people, places, and businesses mentioned in them. These articles are categorized in defined hierarchies so you can quickly discover the content.

Streamlines Customer Support

Businesses receive several complaints and grievances daily, and attending to each is not easy. Nevertheless, with the help of Named Entity Recognition, you can identify the entities in customer grievances, such as product specifications and branch location, to help classify feedback and send it to the respective department responsible for the product under consideration.

Content Recommendation Systems

Named Entity Recognition helps create algorithms for recommendation systems. As you know, these systems make suggestions based on your search history or your current activity.

Named Entity Recognition identifies and extracts entities related to the content in your history and earlier activity. Further, NER compares them with the other unseen content using the label assigned to it. That’s how you regularly see the content of your interest.

Summarizes Resume

You might know some of the tools used to scan a curriculum vitae to collect helpful information, for example, the name, address, and qualification from the curriculum vitae. Most of these tools use Named Entity Recognition to extract such essential details.

Without NER, the recruitment team would have faced several challenges shortlisting prospective candidates. Additionally, with the help of the NER model, they can extract the relevant information.

Further, it reduces the efforts, improves their productivity, and helps shortlist the candidates from thousands of applications received.

Read more: Natural Language Processing (NLP) For Artificial Intelligence

How Do You Implement Named Entity Recognition?

Now that we have learned the applications of NER, we shall closely look into the working of NER models. Let’s go!

There are two basic steps in NER:

• Identification of entities in the text
• Categorizing entities into specific groups

Identification of Entities

The NER model analyses the input text and identifies and locates the named entities. It recognizes sentence boundaries using capitalization rules. Also, it helps contextualize entities within the text, letting the model know their meanings and relationships.

Categorization of Entities

You can also train NER models to classify documents into several types, such as receipts and invoices. Document classification helps NER models make entity recognition more versatile in accordance with the specific characteristics of the document types.

Also, the Named Entity Recognition model uses machine learning algorithms and supervised learning to analyze labeled datasets. Therefore, these datasets guide the model in recognizing similar entities in unseen data.

Also, the NER model refines its knowledge of contextual features, entity patterns, and syntactic structure by improving accuracy over time through multiple training iterations.

Hence, the ability to adapt to new, unseen data lets the NER models manage variations in context, entity types, and language, making them more efficient.

What are the Different Methods of Named Entity Recognition?

Let’s take a look at the most common methods of NER. 

Lexicon Based Method

Named Entity Recognition uses a dictionary with a list of terms to check if any of these words are present in a given text. Nevertheless, this method is not commonly used because it requires regular dictionary updates to keep it precise and accurate.

Rule-based method

The Rule-based Named Entity Recognition method uses a set of predefined rules to support information extraction. These rules can be either pattern-based or context-based.

Pattern-based rules pay attention to the form and structure of words, while context-based rules take into account the context in which a word appears within a text document. Therefore, combining both rules improves the accuracy of the information extraction process in NER.

Machine Learning-Based Method

You can train the model for multi-class classification using different machine learning algorithms. However, it requires a lot of labeling. Further, the model needs an in-depth knowledge of context, making it a daunting task for fundamental machine learning algorithms.

Deep Learning Based Method

Deep learning in Named Entity Recognition models is more precise than the above-mentioned methods as it can combine words. Also, this approach is known as word embedding and can understand the semantic and syntactic relationship between words. In fact, this feature makes deep learning NER highly useful for performing multiple tasks.

Read more: 8 Ways In Which Machine Learning and NLP Accelerates Digital Transformation In The Healthcare Industry

What are the Advantages of NER?

A Named Entity Recognition model offers a wide range of benefits. Here are a few advantages that NER brings along with it. 

• Analysis of important information in unstructured data.
• Prevents human error in analysis
• Improves productivity of employees
• Automates the extraction of information from large datasets
• Enhance the accuracy of Natural Language Processing tasks

Frequently Asked Questions

What are the three common types of NER?

The three major types of NER cover lexicon-based, rule-based, and machine learning-based models.

What are the benefits of NER?

NER has several applications, such as information extraction, text classification, and so forth. For Search Engine Optimization (SEO), NER recognizes named entities and ensures that search engines properly identify and index them.

What are the common challenges in NER?

Often, unclear, misspelled entity names and changes in entity references pose problems in NER.

Get ThinkPalm’s Named Entity Recognition Service

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Using our most advanced NLP and large language models (LLM), you can enhance workflow efficiency and improve productivity to its peak.

Also, our advanced IIoT platform uses NLP algorithms to interpret and act based on natural language instructions to make it more user-friendly and responsive.

Our complete NER solutions support sentiment analysis to properly understand customer feedback and entity recognition for extracting the essential information from text. Get the NER services from ThinkPalm to strengthen your business by guiding you towards smart automation and refined decision-making and delivering a one-of-a-kind customer experience. Contact us today and begin the journey to keep your business alive!

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