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If I say the cat purrs or this cat hunts mice, its perfectly reasonable to also say the kitty purrs or this kitty hunts mice. The context gives you a strong idea that those words are similar. You have to be catlike to purr and hunt mice. So, lets learn to predict a words context. The hope is that a model thats good at predicting a words context will have to treat cat and kitty similarly, and will tend to bring them closer together. The beauty of this approach is that you dont have to worry about what the words actually mean, giving further meaning directly by the company they keep. There are many way to use this idea that similar words occur in similar contexts. In our case, were going to use it to map words to small vectors called embeddings which are going to be close to each other when words have similar meanings, and far apart when they dont. Embedding solves of the sparsity problem. Once you have embedded your word into this small vector, now you have a word representa