[Topic-models] "online" inference in LDA

Sugato Basu sugato at cs.utexas.edu
Tue May 13 14:11:29 EDT 2008


Dave,

Arindam Banerjee and I had a paper where we studied online versions of LDA 
and a couple of other topic models -- mixture of vMF distributions and the 
DCM model. That may be useful for you:

http://www.siam.org/proceedings/datamining/2007/dm07_040Banerjee.pdf

Note that the online inference we used for LDA in this paper was an 
efficient heuristic based on MAP estimates, not the true online Bayesian 
update.

Best,
Sugato.

On Tue, 13 May 2008, Dave Stallard wrote:

> Dear list,
>
> I am interested in applying LDA to a conversational dialog application, in 
> which the "document" is dialog, composed of a sequence of utterances.  The 
> application is "online", meaning that we want to apply inference and find 
> topics in the current utterance, considering only it and the previous 
> utterances of that dialog.  Crucially, we can't look at future utterances, 
> because they haven't happened yet!  So we never see the whole "document" 
> until the dialog is over, just successively longer prefixes of it.
>
> An obvious thing to do is to apply inference repeatedly to the 
> document/dialog as seen so far.  This would redo computation of course, and 
> once we got far enough into the dialog, the gammas would probably be pretty 
> stable.  Any thoughts on this technique, or on the whole enterprise in 
> general? Does it make sense?  Has it been done before?  I've not been able to 
> find anything in the literature on it.
>
> (Thanks for answering my previous query re topic tagging, btw.)
>
>  Dave
>
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