Word Embedding Visualizer

Overview

The Word Embedding Visualizer demonstrates word2vec concepts by showing words as vectors in 2D space. Similar words cluster together based on semantic meaning. Explore word arithmetic (king - man + woman ≈ queen) and nearest neighbors. Essential for understanding modern NLP and neural word representations.

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Tips

• Word embeddings map words to dense vectors in high-dimensional space
• Similar words have similar vectors (cosine similarity)
• Word arithmetic captures semantic relationships
• Example: vector(“king”) - vector(“man”) + vector(“woman”) ≈ vector(“queen”)
• Applications: machine translation, text classification, question answering
• Pre-trained embeddings (Word2Vec, GloVe, FastText) available
• This demo uses simplified 2D projections for visualization