How does Embedding convert text into vectors?
Embedding converts text into dense numerical vectors using mathematical models. It represents words, phrases, or documents as points in a high-dimensional continuous space.
These models are typically trained on vast text corpora, learning semantic and syntactic relationships. Words with similar meanings or contexts are mapped closer together in this vector space. The process involves techniques like dimensionality reduction to capture essential features efficiently. Context is critical, so models often utilize neural networks (e.g., Word2Vec, BERT) to incorporate surrounding words during training. The resulting vectors capture semantic richness beyond simple word matching.
This conversion enables machine learning algorithms to process and analyze text effectively. Applications include similarity search (finding documents with related meaning), semantic clustering, recommendation systems, and powering downstream natural language processing tasks like sentiment analysis or translation by providing meaningful numerical representations of language.
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