Word2Vec Embedding Demo

How Word2Vec Works

Word2Vec represents words as dense vectors (embeddings) in a high-dimensional space, typically ranging from 100 to 300 dimensions. In this demo, we're using 100-dimensional vectors.

These vectors capture semantic relationships between words, where:

• Similar words are positioned closer to each other in the vector space
• Relationships between words are represented by consistent vector offsets
• The famous "king - man + woman = queen" example demonstrates how vector arithmetic can reveal semantic analogies

The model used in this demo contains pre-computed vectors for approximately 1,000 common English words, trained on a large text corpus.