CS5720 - Week 11
Slide 207 of 220

Text Classification Pipeline

A text classification pipeline is a systematic approach to building machine learning models that can automatically categorize text documents into predefined classes.

Let's explore each step of this powerful process!

1
📊
Data Collection
Gather and prepare labeled text data
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2
🔧
Preprocessing
Clean and normalize text data
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3
🎯
Feature Extraction
Convert text to numerical features
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4
🧠
Model Training
Train classification algorithm
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5
📈
Evaluation
Assess model performance
🏗️

Model Architectures

Explore different neural network architectures for text classification

  • Traditional ML: SVM, Naive Bayes, Random Forest
  • Deep Learning: CNN, RNN, LSTM, Transformer
  • Pre-trained Models: BERT, RoBERTa, DistilBERT
  • Ensemble Methods: Voting, Stacking
📊

Evaluation Metrics

Understanding how to measure classification performance

  • Accuracy: Overall correctness
  • Precision: True positives / (True + False positives)
  • Recall: True positives / (True + False negatives)
  • F1-Score: Harmonic mean of precision and recall
⚠️

Common Challenges

Real-world problems and their solutions

  • Imbalanced datasets and class distribution
  • Handling out-of-vocabulary words
  • Domain adaptation and transfer learning
  • Computational efficiency and scalability
🌟

Real-World Applications

How text classification powers modern applications

  • Email spam detection and filtering
  • News categorization and content tagging
  • Customer support ticket routing
  • Social media sentiment monitoring
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Prepared by Dr. Gorkem Kar