Word embeddings are a way to represent words as numerical vectors so that machines can understand their meanings and relationships.
Unlike simple one-hot encoding, embeddings capture semantic similarity, meaning words with similar context have vectors that are close together in space.
How Word Embeddings Work
- Assign each word a vector in a high-dimensional space.
- Train a model (like Word2Vec, GloVe, or FastText) on a large corpus.
- Vectors learn to encode relationships:
- Example: vector(“king”) − vector(“man”) + vector(“woman”) ≈ vector(“queen”)
Embeddings allow machines to understand words beyond their literal form.
Advantages of Word Embeddings
- Captures semantic meaning of words
- Reduces dimensionality compared to one-hot encoding
- Improves performance of NLP models
- Can be pretrained on large datasets and reused
Applications of Word Embeddings
- Text classification (spam detection, sentiment analysis)
- Machine translation
- Named entity recognition (NER)
- Search engines and recommendations
- Chatbots and virtual assistants
Conclusion
Word embeddings are fundamental to modern NLP. They help models understand language contextually, making AI applications more intelligent and accurate.
Citations
https://savanka.com/category/learn/ai-and-ml/
https://www.w3schools.com/ai/