Embedding

Core Idea

An embedding is a way of representing words or concepts as numbers to capture their meaning and relationships in a model.

Explanation

Embeddings are numerical representations of words, phrases, or concepts that capture semantic relationships. In language models, words with similar meanings have similar embeddings, helping the model understand connections between them. Embeddings are widely used in search engines and recommendation systems for their efficient data processing.

Applications/Use Cases

• Document Similarity – Measures how similar documents are for recommendation or retrieval.
• Sentiment Analysis – Helps identify sentiment by mapping word meanings and relationships.
• Recommendation Engines – Matches users with similar content based on shared characteristics.

Related Resources

• “Efficient Estimation of Word Representations in Vector Space” by Mikolov et al. – Foundational paper on word embeddings using the Word2Vec model.

Related People

• Tomas Mikolov – Known for developing Word2Vec, a groundbreaking approach to embeddings in language models.

Related Concepts

• Dense vs Sparse Vectors – Embeddings are usually dense vectors for computational efficiency.
• Cosine Similarity – Often used to compare embeddings based on similarity.
• Token – Embeddings are created for tokens, the units of language in models.