In this module, we'll begin to explore vector space semantics in natural language processing. (This will continue into next week.) Vector space semantics are powerful because they allow us to represent words in a way that allows us to measure similarity between words and capture several other kinds of meaning. We'll start this module by exploring important concepts that underpin this topic, like the distributional hypothesis and term-by-document matrices, and then switch to cover a recent approach to vector space models called word embeddings

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