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Explain Embeddings in NLP

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Problem

Scenario

You are working on an NLP system that needs to turn text into numeric vectors before a downstream model can use it. The team wants a clear explanation of what embeddings are, how they are learned, and why they often work better than sparse text features.

Question

Explain the role of embeddings in NLP systems.

What the candidate should cover

  • Dense vector representations for words, sentences, or documents
  • Why embeddings capture semantic similarity better than one-hot or TF-IDF features
  • Static vs contextual embeddings
  • How embeddings are used in classification, retrieval, clustering, and generation