This is an awesome explanation of those papers! Does anyone have any cool examples of word2vec being used in a project? I'd be interested in seeing what people could make with it.
Document type classification. We wanted to predict which of these k classes a new text document was.
We trained 100-dim word vectors on all the text content we currently have, plus some 30,000 wiki articles related to the business. New content comes in, convert words to vecs, average them, and use that resulting vec as the input to a basic classifier.
For how simple that is, the method is unreasonably good. Widely applicable too.
We trained 100-dim word vectors on all the text content we currently have, plus some 30,000 wiki articles related to the business. New content comes in, convert words to vecs, average them, and use that resulting vec as the input to a basic classifier.
For how simple that is, the method is unreasonably good. Widely applicable too.