AI Lesson 69 – Sequence Modeling in NLP | 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

Sequence Modeling in NLP

Until now, most of the models we discussed treated text as a collection of independent words or features. However, language is naturally sequential. The order of words matters, and meaning often depends on what comes before and after a word. Sequence modeling focuses on learning these dependencies.

This lesson explains why sequence modeling is important in NLP, how it works, and how simple sequence-based models are used in real applications.

Real-World Connection

When your phone predicts the next word while typing a message, it is not just counting words. It considers the sequence of words you have already typed. Similarly, machine translation systems must understand the order of words to generate meaningful sentences.

Without sequence modeling, tasks like speech recognition, translation, and text generation would fail to capture context.

What Is Sequence Modeling?

Sequence modeling is the task of processing input data where the order of elements is important. In NLP, this means understanding how words relate to each other across a sentence or document.

  • Captures word order
  • Learns long-term dependencies
  • Maintains contextual meaning

Why Order Matters in Language

Consider these two sentences:

  • The dog chased the cat
  • The cat chased the dog

Both sentences use the same words, but the meaning is completely different because of word order. Sequence models are designed to capture this difference.

Traditional vs Sequence-Based Models

Traditional models like Bag of Words ignore order. Sequence models process text step by step, preserving context.

  • Bag of Words ignores sequence
  • TF-IDF ignores position
  • Sequence models process words in order

A Simple Sequence Modeling Example

Below is a very basic illustration showing how word sequences can be represented numerically. This is not a deep learning model yet, but it shows the idea of preserving order. 


sentences = [
    ["i", "love", "ai"],
    ["ai", "loves", "data"]
]

word_to_index = {"i": 1, "love": 2, "ai": 3, "loves": 4, "data": 5}

encoded_sentences = [
    [word_to_index[word] for word in sentence]
    for sentence in sentences
]

print(encoded_sentences)
[[1, 2, 3], [3, 4, 5]]

Understanding the Code

Each word is mapped to a unique number, and sentences are converted into ordered sequences of numbers. The sequence order is preserved, which is essential for training sequence models like RNNs.

Types of Sequence Modeling Tasks

  • Next word prediction
  • Named entity recognition
  • Machine translation
  • Text generation
  • Speech recognition

Challenges in Sequence Modeling

Sequence modeling introduces several challenges:

  • Handling long sequences
  • Maintaining long-term context
  • Computational complexity

These challenges led to the development of specialized neural architectures like RNNs, LSTMs, and GRUs, which we will explore in upcoming lessons.

Practice Questions

Practice 1: What do we call modeling where word order matters?



Practice 2: What key aspect of language do sequence models preserve?



Practice 3: What helps sequence models understand meaning over time?



Quick Quiz

Quiz 1: What makes sequence modeling different from Bag of Words?





Quiz 2: Which task relies heavily on sequence modeling?





Quiz 3: What do sequence models maintain to understand text?





Coming up next: RNN, LSTM, and GRU in NLP — neural networks designed specifically for sequences.

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