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We have a large collection (1-2M) of documents of various domains (politics, design, programming, etc.). And lets assume that we don't know the exact number of domains. And our goal is to build relevant topic models for these domains. For example I want to find N topics in each domain. For politics it could be: foreign policy, government, economic policy, etc. For design domain: web design, architecture, etc. And so on.

If I understand correctly, when using a LDA it is necessary to form a training set for specified domain (e.g. politics). But problem arises if there is no information about domain. How can I form a training set in that case? I was thinking about building index, extracting documents, that contain significant words (defined by TF-IDF). But it seems that it is not the best solution.

If instead of LDA we can use HDP and handle training set compiled with random documents. But there arises another problem - a huge vocabulary, which may not fit in the memory (we need much more words to describe several domains, than one).

So the problem is: what is the best way to build topic models (or one topic model?) of various (and unknown) domains? And them apply this models to new (unseen) documents.

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    $\begingroup$ Please unfold abbreviations "LDA", "TF-IDF", "HDP" in yout question. Not everybody knows these acronyms. $\endgroup$ – ttnphns Nov 30 '12 at 6:45
  • $\begingroup$ @ttnphns, I've added links to wikipedia. $\endgroup$ – peppered Nov 30 '12 at 10:20
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Hierarchical topic models are designed precisely for this task. These models infer topic hierarchies where high level topics (poltics, sports) have subtopics beneath them (foreign policy, baseball).

The original formulation is from this paper

And this very recent formulation, which is able to infer more accurate hierarchical structures

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  • $\begingroup$ Thanks for your answer! But how can I form a training set (from a huge collection), which would cover all domains? Or it supposed to use all (about 2 millions) documents in original collection? $\endgroup$ – peppered Nov 29 '12 at 21:29
  • $\begingroup$ Yes, the idea is that it will discover the tree structure on it's own from the entire document collection. $\endgroup$ – jerad Nov 29 '12 at 21:36
  • $\begingroup$ If I understand correctly for big collections it should be used an online version of hLDA (or nHDP)? $\endgroup$ – peppered Nov 29 '12 at 21:41
  • $\begingroup$ @pepper, I don't think that's the case. What makes you think that? On-line inference is generally more difficult than batch processing because of the constraints on computing time. $\endgroup$ – jerad Nov 29 '12 at 21:44
  • $\begingroup$ I'm new in this area, so it was my supposition. If to be honest, I did not think that there is a big difference between online and batch processing. $\endgroup$ – peppered Nov 29 '12 at 21:54

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