Definition
Document classification is the process of categorizing text documents into predefined classes using machine learning or rule-based methods. Classes may include topics, spam detection, or sentiment.
Purpose
The purpose is to organize and filter large volumes of text efficiently. It supports search, content moderation, and automated workflows.
Importance
- Saves time by automating categorization.
- Key for email spam filtering, legal discovery, and knowledge management.
- Errors may lead to missed or misclassified documents.
- Related to NLP tasks like sentiment analysis.
How It Works
- Collect and preprocess text documents.
- Represent text with features (e.g., TF-IDF, embeddings).
- Train classification models (SVMs, neural networks).
- Validate model accuracy on labeled test sets.
- Deploy classifier to categorize new documents.
Examples (Real World)
- Gmail spam filter: classifies emails into spam and non-spam.
- News aggregators: categorize articles by topic.
- Legal tech: classifies documents for discovery and compliance.
References / Further Reading
- Manning et al. Introduction to Information Retrieval. Cambridge University Press.
- Jurafsky & Martin. Speech and Language Processing. Stanford.
- IEEE Transactions on Knowledge and Data Engineering.