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From this "Cosine similarity measures the degree to which two vectors point in the same direction, regardless of magnitude. When vectors point in the same direction, cosine similarity is 1; when vectors are perpendicular, cosine similarity is 0; and when vectors point in opposite directions, cosine similarity is -1. In positive space, cosine similarity is ...


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The easiest way is to treat all out-of-vocabulary terms as a specific term in your matrix (i.e. "OOV"). So for instance, if my training data contains 3 words: "I", "like", "cake", my document-term matrix would contain 4 items, "I", "like", "cake", and "OOV".


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