AI Lesson 62 – Text Processing & Normalization | 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

Text Processing

Before machines can understand human language, raw text must be cleaned and prepared. This preparation stage is called text processing. It is one of the most important steps in Natural Language Processing because models perform poorly if the input text is noisy or inconsistent.

In real-world data, text often contains punctuation, mixed cases, extra spaces, numbers, emojis, and irrelevant words. Text processing converts this raw input into a structured and usable format.

Real-World Connection

Think about customer reviews on an e-commerce website. Some users write in uppercase, some include emojis, some use abbreviations, and some make spelling mistakes. Before analyzing sentiment or intent, the system must first clean and standardize this text. That is exactly what text processing does.

Why Text Processing Is Necessary

Raw text creates confusion for machines. The words “AI”, “ai”, and “Ai” look different to a computer, even though they mean the same thing to humans. Text processing removes this inconsistency.

  • Reduces noise in text data
  • Improves model accuracy
  • Creates consistent input format
  • Reduces vocabulary size

Common Text Processing Steps

Most NLP pipelines follow these basic text processing steps:

  • Lowercasing text
  • Removing punctuation
  • Removing extra spaces
  • Removing numbers or symbols
  • Tokenization

Lowercasing Text

Lowercasing converts all characters to lowercase so that words are treated uniformly. 


text = "Natural Language Processing Is POWERFUL"
processed = text.lower()
print(processed)
natural language processing is powerful

Removing Punctuation

Punctuation usually does not add meaning for most NLP tasks. Removing it simplifies the text. 


import string

text = "Hello, world! NLP is amazing."
clean_text = text.translate(str.maketrans("", "", string.punctuation))
print(clean_text)
Hello world NLP is amazing

Removing Extra Spaces

Text data may contain unnecessary spaces that should be removed to maintain consistency. 


text = "NLP    is    very    useful"
clean_text = " ".join(text.split())
print(clean_text)
NLP is very useful

Tokenization

Tokenization breaks text into smaller units called tokens, usually words. This allows machines to analyze text piece by piece. 


sentence = "Text processing prepares data"
tokens = sentence.split()
print(tokens)
['Text', 'processing', 'prepares', 'data']

Combining Text Processing Steps

In real projects, these steps are usually combined into a pipeline so that text is cleaned automatically before analysis. 


import string

text = "NLP, IS Powerful!!!"

text = text.lower()
text = text.translate(str.maketrans("", "", string.punctuation))
tokens = text.split()

print(tokens)
['nlp', 'is', 'powerful']

Practice Questions

Practice 1: What is the process of cleaning and preparing text called?



Practice 2: Why do we convert text to lowercase?



Practice 3: What is splitting text into words called?



Quick Quiz

Quiz 1: What is the main goal of text processing?





Quiz 2: What is commonly removed during text processing?





Quiz 3: What are individual words after tokenization called?





Coming up next: Tokenization — breaking text into words, subwords, and characters.

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