AI Lesson 76 – Named Entity Recognition | Dataplexa

AI Course

I. AI Foundations
1. Introduction to AI 2. History of AI 3. AI vs ML vs DL 4. Search Algorithms 5. Intelligent Agents 6. Informed & Uninformed Search 7. Constraint Satisfaction 8. Adversarial Search 9. Logic in AI 10. Planning in AI 11. Knowledge Representation 12. Probabilistic AI 13. Bayesian Networks 14. Markov Models 15. AI Ethics 16. AI Applications 17. Intro to PyTorch 18. Intro to TensorFlow 19. AI Workflows 20. Data for AI
II. Machine Learning
21. Introduction to ML 22. ML Workflow 23. Supervised Learning 24. Unsupervised Learning 25. Reinforcement Learning 26. Linear Regression 27. Logistic Regression 28. SVM 29. Decision Trees 30. Random Forest 31. Gradient Boosting 32. XGBoost 33. K-Means 34. Hierarchical Clustering 35. Dimensionality Reduction 36. Feature Engineering 37. Feature Selection 38. Model Evaluation 39. Overfitting vs Underfitting 40. Cross Validation
III. Deep Learning
41. Intro to Deep Learning 42. Neural Networks 43. Activation Functions 44. Backpropagation 45. Loss & Optimizers 46. Regularization 47. CNN Basics 48. CNN Architectures 49. RNN, LSTM, GRU 50. Seq2Seq 51. Transformers 52. Attention 53. Positional Encoding 54. Training DL 55. Hyperparameter Tuning 56. Transfer Learning 57. Autoencoders 58. GAN Basics 59. GAN Applications 60. Model Serving
IV. Natural Language Processing
61. Intro to NLP 62. Text Processing 63. Tokenization 64. Stopwords & Lemmatization 65. BoW & TF-IDF 66. Word Embeddings 67. Sentence Embeddings 68. NLP Classification 69. Sequence Modeling 70. RNN in NLP 71. Transformers in NLP 72. BERT Basics 73. BERT Fine-tuning 74. RoBERTa & DistilBERT 75. Sentiment Analysis 76. NER 77. Machine Translation 78. Text Generation 79. NLP Evaluation 80. NLP Use Cases
V. Computer Vision
81. Intro to CV 82. Image Processing 83. Filters & Edges 84. Convolutions 85. Object Detection 86. Object Tracking 87. Image Segmentation 88. Face Recognition 89. Feature Detection 90. OCR 91. Image Augmentation 92. Generative Vision 93. Vision Transformers 94. Multimodal Models 95. CV Use Cases
VI. LLM Engineering
96. Intro to LLMs 97. Tokenization 98. LLM Architecture 99. Pretraining 100. Fine-tuning 101. RLHF 102. Prompt Engineering 103. Embedding Models 104. Vector Databases 105. LLM Agents 106. Guardrails & Safety 107. AI in Production 108. End-to-End Project

Lesson 76: Named Entity Recognition (NER)

Named Entity Recognition, commonly called NER, is a core task in Natural Language Processing that focuses on identifying important real-world entities mentioned in text.

Instead of just understanding words, NER helps machines understand who, what, and where the text is talking about.

Real-World Connection

Whenever Google highlights people, locations, or organizations in search results, or when resumes are automatically scanned for names and companies, Named Entity Recognition is working behind the scenes.

NER is widely used in news analysis, legal document processing, healthcare records, chatbots, and search engines.

What Is Named Entity Recognition?

Named Entity Recognition is the process of identifying and classifying entities in text into predefined categories.

  • Person names
  • Organizations
  • Locations
  • Dates and time
  • Money and quantities

The goal is not just to find words, but to understand their role in the sentence.

How NER Works

Modern NER systems use deep learning models, especially transformer-based architectures like BERT. These models analyze each word in context and predict whether it belongs to an entity.

For example, the word “Apple” can mean a fruit or a company, and the model decides based on surrounding words.

Simple NER Example

Let’s see how a pretrained model extracts entities from text. 


from transformers import pipeline

ner_model = pipeline("ner", grouped_entities=True)

text = "Sundar Pichai is the CEO of Google headquartered in California"
entities = ner_model(text)

for entity in entities:
    print(entity)
{'entity_group': 'PER', 'word': 'Sundar Pichai'} {'entity_group': 'ORG', 'word': 'Google'} {'entity_group': 'LOC', 'word': 'California'}

Understanding the Output

Each detected entity includes its type and the text it represents. The model groups tokens belonging to the same entity and assigns a label.

  • PER — Person
  • ORG — Organization
  • LOC — Location

Why NER Is Important

NER converts unstructured text into structured data. This allows systems to perform search, analytics, and automation more efficiently.

Without NER, machines would only see text as words, not meaningful entities.

Common Use Cases

  • Resume and document parsing
  • Information extraction from news articles
  • Healthcare record analysis
  • Customer support automation

Challenges in Named Entity Recognition

  • Ambiguous words with multiple meanings
  • New or unseen entity names
  • Domain-specific entities

Fine-tuning models on domain-specific data helps improve accuracy.

Practice Questions

Practice 1: What does NER identify in text?



Practice 2: What type of entity is “Elon Musk”?



Practice 3: What helps NER models decide entity meaning?



Quick Quiz

Quiz 1: Which NLP task extracts names and locations?





Quiz 2: What entity type is “Microsoft”?





Quiz 3: NER helps convert what into structured data?





Coming up next: Machine Translation — how AI converts text from one language to another.

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