Description

Word embedding is a technique used in natural language processing to represent words as dense vectors in a continuous vector space. These representations capture semantic relationships between words, allowing similar words to have similar vector representations. Word embeddings enable machine learning models to work with text data more effectively by transforming words into a format that preserves semantic meaning and allows for mathematical operations.

Examples

• 📊 Word2Vec
• 🔤 GloVe
• 📚 FastText

Applications