What is word embedding, and what are Word2Vec and GloVe?

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Word Embedding is an NLP technique where words are represented as dense vectors of real numbers instead of sparse one-hot encodings. The idea is that words with similar meanings will have similar vector representations in a high-dimensional space. This helps machine learning models understand semantic relationships between words.

Example:

• Traditional one-hot: "king" → [0,0,0,1,0,...] (no meaning captured).

• Embedding: "king" and "queen" will have vectors close in space, capturing gender relation.

Word2Vec

• Developed by Google (2013).

• Learns embeddings using neural networks.

• Two main models:

  • CBOW (Continuous Bag of Words): Predicts a word from its context.

  • Skip-Gram: Predicts context words given a word.

• Captures semantic relationships like:

  • vector("king") - vector("man") + vector("woman") ≈ vector("queen").

GloVe (Global Vectors for Word Representation)

• Developed by Stanford.

• Based on matrix factorization of word co-occurrence statistics across the entire corpus.

• Focuses on capturing global statistical information instead of just local context.

• Produces embeddings where semantic similarity and analogy reasoning are preserved.

Key Difference

• Word2Vec → Predictive model (local context, neural-based).

• GloVe → Count-based model (global co-occurrence, matrix factorization).

👉 In short, word embeddings like Word2Vec and GloVe enable NLP systems to understand meaning, similarity, and analogies, making them the foundation for modern AI language models.

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