AI Lesson 63 – Tokenization Techniques | 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

Tokenization

Tokenization is one of the most fundamental steps in Natural Language Processing. It is the process of breaking text into smaller units called tokens. These tokens can be words, parts of words, characters, or even sentences.

Machines cannot understand full paragraphs directly. Tokenization helps convert continuous text into manageable pieces that models can analyze and learn from.

Real-World Connection

When you search for something on Google, the search engine first breaks your query into individual words before matching them with web pages. Similarly, chatbots split your messages into tokens to understand intent. Tokenization is the starting point for almost every NLP system.

Why Tokenization Is Important

Without tokenization, text remains an unstructured string. Tokenization creates structure and enables further processing.

  • Makes text understandable for machines
  • Helps identify important words
  • Enables feature extraction
  • Improves model performance

Types of Tokenization

There are different ways to tokenize text depending on the problem:

  • Word tokenization
  • Sentence tokenization
  • Character tokenization
  • Subword tokenization

Word Tokenization

Word tokenization splits text into individual words. This is the most common and simplest form of tokenization. 


sentence = "Tokenization is essential in NLP"
tokens = sentence.split()
print(tokens)
['Tokenization', 'is', 'essential', 'in', 'NLP']

What the Code Is Doing

The sentence is split wherever there is a space. Each resulting word becomes a token. This method is simple but may not handle punctuation or special cases well.

Sentence Tokenization

Sentence tokenization splits a paragraph into individual sentences. This is useful for summarization and document analysis. 


text = "NLP is powerful. It helps machines understand language."
sentences = text.split(". ")
print(sentences)
['NLP is powerful', 'It helps machines understand language.']

Character Tokenization

Character tokenization breaks text into individual characters. It is often used in language modeling and low-level text analysis. 


word = "NLP"
chars = list(word)
print(chars)
['N', 'L', 'P']

Subword Tokenization

Subword tokenization breaks words into smaller meaningful units. This helps handle unknown words and rare vocabulary. Modern NLP models like BERT and GPT rely heavily on subword tokenization.

For example, the word “unbelievable” might be split into:

  • un
  • believe
  • able

Challenges in Tokenization

Tokenization is not always straightforward. Language has many complexities.

  • Punctuation handling
  • Contractions like “don’t”
  • Multiple languages
  • Special symbols and emojis

Practice Questions

Practice 1: What is the process of breaking text into smaller units called?



Practice 2: What are the individual units created after tokenization?



Practice 3: Which type of tokenization is used in modern NLP models?



Quick Quiz

Quiz 1: What is the main purpose of tokenization?





Quiz 2: Word tokenization splits text into?





Quiz 3: Why is subword tokenization useful?





Coming up next: Stopwords, Stemming, and Lemmatization — reducing words to their meaningful form.

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