What are word embeddings? They are high-dimensional vectors generated by a neural network to represent words. These vectors may appear random but are integral to modern NLP models. How do we interpret them? Natural language is immensely nuanced, and word meaning relies on context. Representing each word with a unique number is insufficient to capture this complexity. In the past, NLP researchers manually engineered features, such as using a feature vector like [taste: sweet, color: red, season: winter] to represent 'apple' in a fruit context. This vector represents three dimensions relevant to fruits. However, generating these features was labor-intensive.

In contrast, neural networks are able to learn word embeddings automatically, providing similar feature representation. These embeddings are generated by training a neural network to predict the next word given a sequence of words. Through this process, the network learns an efficient representation for those words.

Word embeddings possess a rich semantic representation for words. For instance, if you perform the computation 'king - man + woman,' the result is 'queen.' When plotted in a 2D space, similar words cluster together. Fruit names cluster nearby, while country names occupy another region. This demonstrates that word embeddings learn distinct representations for natural language.

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