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One option is to train a single embedding space with all the information. If you use Word2Vec in Genism, positive and negative operations are built-in. That is similar to how word analogies are calculated. The code would be something like: import gensim word2vec_model = gensim.models.Word2Vec(docs) word2vec_model.most_similar(positive=['Total War', '...


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You are absolutely right - the "distance" between word embeddings does not automatically imply they are automatically semantically similar! This is a common misconception. This is more a result of when you train towards an objective, certain words can be used in place of one another and as a result, they can have similar representations inside of a ...


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Looks like I found Google's paper here.


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