What is word embedding?

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Word embedding is a technique in Natural Language Processing (NLP) that represents words as dense numerical vectors in a continuous vector space, capturing semantic meaning and relationships between words.

Purpose

  • Computers cannot understand raw text directly; they need numbers.

  • Word embeddings convert words into vectors of numbers where similar words have similar representations.

How It Works

  • Each word is mapped to a fixed-length vector (e.g., 100 or 300 dimensions).

  • Words with similar meanings are close together in the vector space.

  • For example:

    • Vector for “king” − Vector for “man” + Vector for “woman” ≈ Vector for “queen”

Popular Methods

  1. Word2Vec

    • Predicts a word based on its context (CBOW) or predicts context based on a word (Skip-gram).

  2. GloVe (Global Vectors)

    • Uses word co-occurrence statistics from large corpora to learn embeddings.

  3. FastText

    • Builds embeddings for subwords, so it can handle out-of-vocabulary words.

  4. Contextual embeddings (BERT, GPT, etc.)

    • Word vectors depend on context, so the same word can have different vectors in different sentences.

Benefits

  • Captures semantic similarity (e.g., “cat” is closer to “dog” than “car”).

  • Enables NLP tasks like text classification, sentiment analysis, machine translation, and question answering.

  • Reduces dimensionality compared to one-hot encoding.

✅ Summary:
Word embeddings turn words into mathematical vectors that encode meaning and relationships, allowing NLP models to process text effectively and intelligently.

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