How do we convert text into numbers that machines can understand? Text representation is the bridge between human language and mathematical computation.
From simple counting methods to sophisticated neural embeddings, let's explore the evolution of text representation!
Simple binary vectors where each dimension represents one word in the vocabulary
Count-based representation that captures word frequency but ignores order
Weighted representation that balances term frequency with document frequency
Dense vector representations that capture semantic relationships between words
Dynamic representations that change based on surrounding context
Character or subword-level embeddings for handling unknown words
| Method | Pros | Cons | Best Use Cases |
|---|---|---|---|
| One-Hot | Simple, interpretable | Sparse, no semantics | Small vocabularies, categorical features |
| Bag of Words | Fast, interpretable, baseline | No word order, sparse | Document classification, sentiment analysis |
| TF-IDF | Handles common words well | Still sparse, no semantics | Information retrieval, search engines |
| Word Embeddings | Dense, semantic, efficient | Fixed context, training needed | Most NLP tasks, similarity computation |
| Contextual | Context-aware, SOTA performance | Computationally expensive | Complex NLP, ambiguous words |