Description

Distributional semantics is an approach to semantics that relies on the distributional hypothesis, which states that words that occur in similar contexts tend to have similar meanings. This theory forms the basis for many modern natural language processing techniques, including word embeddings. By analyzing the statistical patterns of word co-occurrences in large text corpora, distributional semantic models can capture semantic relationships between words and represent them in a way that's useful for various NLP tasks.

Examples

• 📊 Word co-occurrence matrices
• 🔤 Latent Semantic Analysis
• 📈 Word embeddings

Applications