Hi all,
I'm working in document classification and I wonder if there is a way of
having the feature vector calculated based on Latent Semantic Indexing
(LSI) instead of tf or tf-idf. As you know with LSI or Latent Dirichlet
Allocation (LDA), semantic features are captured.
I found an online Python library to do so called gensim. The point is, how
to merge gensim with sklearn to fullfill the requirement? or any
alternatives?
Jack
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