Word embeddings represent words as vectors where similar words appear in similar contexts and thus have similar vector directions, while unrelated words have perpendicular vectors, indicating that word vectors capture semantic relationships through their geometric orientation in vector space.
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Now that words are represented in an embedding as vectors, what do they actually tell us about the words and their meaning? Recall the word vectors are built from different text sources.
If two words appear in similar texts, they would have similar coordinate compositions. If we disregard the number of times the words appear and just focus on the fact that they appear in similar documents, then the word vectors would point in similar directions. Those words would be related and share similar meanings or contexts. Alternatively, if the vectors were pointed in perpendicular directions, they would not be present in similar contexts, so they would not be related.
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