What Is POS and NER in NLP? 

POS (Part-of-Speech) tagging and NER (Named Entity Recognition) are core techniques in Natural Language Processing used to understand text. POS tagging labels each word with its grammatical role, such as nouns, verbs, or adjectives. NER identifies and categorizes important information like people, places, and organizations into predefined entity types. 

In this blog, you will learn what is POS and NER in NLP, how they work, how they differ, and why they are essential in modern Artificial Intelligence systems.   

What Is POS and NER in NLP? 

Before diving deeper, it’s important to understand the core idea behind this topic. When you ask what is POS and NER in NLP, you are looking at two foundational techniques that help machines interpret text more intelligently. Let’s explore how each contributes to language understanding. 

• POS (Part-of-Speech tagging) identifies the grammatical role of each word in a sentence. 
• NER (Named Entity Recognition) detects and classifies real-world entities such as names, locations, dates, and organizations. 

Both techniques allow machines to move beyond simple keyword detection. They help systems understand how words function and what important information they represent. 

Here is a quick comparison: 

Technique	Focus	Example Output
POS Tagging	Grammar role	“run” → Verb
NER	Real-world entity	“Amazon” → Organization

• POS focuses on sentence structure and syntax. It answers: What role does this word play? 
• NER focuses on extracting meaningful information. It answers: What real-world concept does this phrase represent? 

Together, these techniques form a foundation for deeper language understanding in NLP systems. 

Also Read: 15+ Top Natural Language Processing Techniques 

What Is POS Tagging? 

To understand what is POS and NER in NLP, you must first understand POS tagging. It focuses on sentence structure and grammar. It helps machines recognize how each word functions within a sentence. 

POS tagging assigns a grammatical label to every word. 

Common POS tags include: 

• Noun 
• Verb 
• Adjective 
• Adverb 
• Pronoun 
• Preposition 

Also Read: Natural Language Processing Information Extraction 

Example: 

Sentence: 
“Riya bought a new laptop.” 

POS tagging output: 

• Riya → Noun 
• bought → Verb 
• new → Adjective 
• laptop → Noun 

By labeling each word, the system learns the structure of the sentence. It can identify subjects, actions, and descriptions. 

When discussing what is POS and NER in NLP, POS tagging explains how sentences are built grammatically, forming the foundation for deeper language analysis. 

Also Read: What is Natural Language Understanding & How it Works? 

What Is Named Entity Recognition (NER)? 

To fully understand what is POS and NER in NLP, you also need to understand NER. While POS focuses on grammar, NER focuses on meaning. It identifies important real-world entities mentioned in text. 

Named Entity Recognition detects and classifies specific information into predefined categories. 

Also Read: Named Entity Recognition(NER) Model with BiLSTM and Deep Learning in NLP 

Common NER categories include: 

• Person 
• Organization 
• Location 
• Date 
• Money 
• Event 

Example: 

Sentence: 
“Microsoft opened a new office in Toronto in 2024.” 

NER output: 

• Microsoft → Organization 
• Toronto → Location 
• 2024 → Date 

When discussing what is POS and NER in NLP, NER explains how machines identify meaningful entities that carry important information inside a sentence. 

Also Read: Types of Natural Language Processing with Examples 

Key Differences Between POS and NER 

Although both techniques analyze text, they solve different problems. Understanding these differences makes it clearer how they work together in NLP systems. 

Here is a structured comparison: 

Aspect	POS Tagging	NER
Focus	Grammatical structure	Real-world entities
Level	Word-level grammar	Phrase-level entities
Output	Noun, Verb, Adjective	Person, Location, Organization
Goal	Understand syntax	Extract key information

Both techniques are often combined in NLP pipelines. POS helps structure the sentence, while NER extracts important information from it. 

Also Read: NLP Models in Machine Learning and Deep Learning    

Conclusion  

POS and NER are foundational components of Natural Language Processing. When you understand what is POS and NER in NLP, you see how machines analyze both grammar and meaning. POS tagging explains how words function in a sentence, while NER identifies key real-world entities. Together, they power modern AI applications that rely on structured language understanding.  

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