What are word embeddings (Word2Vec, GloVe)?

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What are Word Embeddings?

Word embeddings are a way to represent words as numerical vectors in a continuous space, where words with similar meanings are placed closer together.

  • They allow machines to understand semantic relationships between words.

  • Traditional approaches like one-hot encoding don’t capture meaning or context, but embeddings do.

Example:

  • “King” and “Queen” are related concepts.

  • In embedding space, their vectors will be close, and relationships like King − Man + Woman ≈ Queen can be captured.

Popular Word Embedding Models

1. Word2Vec

  • Developed by Google in 2013.

  • Converts words into vectors using neural networks.

  • Two main approaches:

    1. CBOW (Continuous Bag of Words): Predicts a word based on its surrounding context.

    2. Skip-gram: Predicts the surrounding context words given a target word.

  • Captures semantic meaning and relationships efficiently.

2. GloVe (Global Vectors for Word Representation)

  • Developed by Stanford in 2014.

  • Uses matrix factorization on a word co-occurrence matrix from a large corpus.

  • Captures both global statistics (overall word co-occurrence) and local context (window-based similarity).

  • Effective in capturing linear relationships like Word2Vec, but emphasizes global corpus structure.

Why Word Embeddings are Useful

  • Represent words in a dense, low-dimensional space (reduces memory usage).

  • Capture semantic similarity between words.

  • Used in NLP tasks like:

    • Sentiment analysis

    • Machine translation

    • Question answering

    • Text classification

✅ Summary:
 Word embeddings are vector representations of words that capture their meaning and relationships. Word2Vec uses local context with neural networks, while GloVe uses global co-occurrence statistics. Both are foundational tools in modern NLP.

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