Word2Vec
A pioneering neural network model developed by Google in 2013 that revolutionized NLP by efficiently learning high-quality word embeddings from text.
Think of It Like This
Like a detective figuring out the true identity of a masked person strictly by observing exactly who they choose to hang out with.
Word2Vec uses two self-supervised architectures: Continuous Bag-of-Words (predicting a target word from its context) and Skip-Gram (predicting context words from a target word). Despite its simplicity, it captured complex linguistic regularities. It proved that shallow neural networks could learn deep semantic representations from vast unlabeled corpora.