What is named entity recognition (NER)?

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Named Entity Recognition (NER) is a core task in Natural Language Processing (NLP) that involves identifying and classifying specific pieces of information (entities) in text into predefined categories.

🔑 Definition

NER scans text and detects entities such as:

• Person names → “Elon Musk”

• Organizations → “Google”

• Locations → “New York”

• Dates & Times → “September 2, 2025”

• Monetary Values → “$1,000”

• Other domain-specific entities (e.g., diseases in medical text, chemical compounds in scientific text).

⚙️ Example

Sentence: “Apple is planning to open a new office in London in 2025.”

• Apple → Organization

• London → Location

• 2025 → Date

🔨 How NER Works

1. Tokenization – Split text into words/tokens.

2. Feature Extraction / Embeddings – Represent tokens numerically (e.g., word2vec, BERT embeddings).

3. NER Model – Algorithms like:

   • Rule-based systems (dictionaries, regex).

   • Machine learning (CRF, SVM).

   • Deep learning (BiLSTM-CRF, Transformers like BERT).

4. Classification – Assign labels (PER, LOC, ORG, DATE, etc.) to each token.

🚀 Applications of NER

• Search Engines → Improve relevance by recognizing entities.

• Chatbots/Virtual Assistants → Extract key info from user queries.

• Information Extraction → From resumes, legal documents, medical records.

• Business Intelligence → Identify companies, products, or competitors in news.

👉 In short: NER teaches machines to recognize “who,” “where,” “when,” and “what” in unstructured text.

🔑Read More:

What is NLP?

What is the role of attention in NLP models?

What are transformers in NLP?

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