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One way to test your embedding is see how often your model agrees with the common consensus of how other embeddings complete word analogies. A collection of established word embedding analogies are here.


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The reason to average the embedded vectors of the words in a paragraph or document is to obtain a single fixed-size vector that represents the whole text. Then, the document-level vector can be used as input to a document classification model or any other document-level model. If you explicitly want to compute word-level representations and then combine them ...


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