title: NLP: Find the most similar Corpus (not Document)
body: <p>I'm looking for suggestions on how to approach a document classification problem.  I will explain by means of example:</p>
  <p><strong>Problem statement</strong></p>
  <p>I have a collection of papers published by a university.  I have another collection published by another university.  And so on, for many universities.</p>
  <p>When a new paper comes in, I'd like to determine which university probably published it.</p>
  <p><strong>My current approach</strong></p>
  <ol>
  <li>For each university, build a dictionary of all terms from all papers with frequencies.  Preprocess into terms and build a gensim <code>Dictionary</code> per university.</li>
  <li>Build a &quot;master&quot; dictionary by merging all of the university-specific dictionaries.  Iterate and perform <code>master_dictionary.merge_with(university_dictionary)</code></li>
  <li>Treat each university dictionary as a document in a new corpus. Turn it into a BoW representation, and build a model from that.  TfIdf/LSI/LDA.</li>
  <li>Perform a similarity match of the new paper (as BoW) against the model.  Find the university dictionary document that matches closest.</li>
  </ol>
  <p><strong>My question</strong></p>
  <p>How is a problem like this tackled? (And is there a name for this?)</p>
  <ul>
  <li>I'm currently comparing a new document with a summarized document, one per university.</li>
  <li>I could compare a new document with every document across all universities.  Then get the universities for the top matching documents using metadata.</li>
  <li>I've run across the Author-Topic Model but haven't looked into it, but that seems like this may be a good fit.</li>
  </ul>
  <p>Any other ideas?</p>