Word embeddings are a fundamental concept in Natural Language Processing (NLP), transforming text into numerical vectors that capture semantic relationships. This guide will walk you through the basics, popular models, and applications of word embeddings.

🌟 What Are Word Embeddings?

Word embeddings represent words in a continuous vector space, where semantically similar words are located closer to each other. Unlike one-hot encoding, they capture nuanced meanings and context.

word_embeddings

Key Features:

  • Dimensionality Reduction: Converts text into dense vectors (e.g., 100-300 dimensions).
  • Semantic Understanding: Enables models to grasp word relationships (e.g., "king" - "man" + "woman" ≈ "queen").
  • Efficiency: Reduces computational complexity for text processing tasks.

🧠 Popular Word Embedding Models

  1. Word2Vec
    Developed by Google, it uses neural networks to generate embeddings.

    word2vec
    [Learn more about Word2Vec](/en/resources/nlp-tutorials/intro)

  2. GloVe
    Based on global word co-occurrence statistics, it provides fast and effective training.

    glove

  3. FastText
    Extends Word2Vec by considering subword information for better performance.

    fasttext

  4. BERT
    A transformer-based model that generates contextual embeddings.

    bert
    [Explore BERT in depth](/en/resources/nlp-tutorials/transformers)

📈 Applications of Word Embeddings

  • Text Classification: Improve model accuracy by using semantic features.
  • Machine Translation: Facilitate language understanding between pairs.
  • Sentiment Analysis: Capture nuanced emotional tones in text.
  • Recommendation Systems: Use embeddings to relate user preferences.

📚 Expand Your Knowledge

For advanced topics on word embeddings and their implementations:
Advanced Word Embedding Techniques

Note: All images are illustrative and generated for educational purposes.