Hey juxtaposicion, fascinating work. I have many questions so I am just going to shoot them rapid fire.
What is the dimensionality of each word vector and what does a words position in this space "mean"? What is this dimensionality determined by? Have your tried any dimensionality reduction algorithms like PCA or Isomap? It would be interesting to find the word vectors that contain the most variation across all of wikipedia. Have you tried any other nearest neighbor search methods other than a simple dot product, such as locality sensitive hashing?
I guess most of those questions are about the word2vec algorithm, but you are probably in a good place to answer them after working with it. Anyways, cool work, and I am glad you did it in python so I can really dig in and understand it.
What is the dimensionality of each word vector and what does a words position in this space "mean"? What is this dimensionality determined by? Have your tried any dimensionality reduction algorithms like PCA or Isomap? It would be interesting to find the word vectors that contain the most variation across all of wikipedia. Have you tried any other nearest neighbor search methods other than a simple dot product, such as locality sensitive hashing?
I guess most of those questions are about the word2vec algorithm, but you are probably in a good place to answer them after working with it. Anyways, cool work, and I am glad you did it in python so I can really dig in and understand it.