[Topic-models] On author topic model

Wray Buntine wray.buntine at monash.edu
Thu Jul 20 20:41:11 EDT 2017


We did something like this in
      Kar Wai Lim and Wray Buntine. Bibliographic analysis on research
publications using authors, categorical labels and the citation network.
Machine Learning, 103:185–213, 2016.

We thought the ATM was a bit primitive so added the extra bit as
"non-parametric ATM" in our experiments.  Its pretty simple to implement.
Our own model does a lot more.  Implemented using HDPs/HPYPs which are
pretty efficient when done right ... avoid HCRPs like the plague.

Prof. Wray Buntine
Course Director for Master of Data Science
Monash University

On 21 July 2017 at 10:20, Eric Kang <erickangnz at gmail.com> wrote:

> Hi everyone,
> I have a question about the author-topic model. Is my understanding
> correct that the author-topic probabilities are "constant" across different
> documents? So if the same author writes multiple documents, the implied
> document-topic proportions would be the same between those documents?
> I thought perhaps another model might be to suppose that author-topic
> probabilities are a multinomial random variable (with a Dirichlet prior)
> that is sampled per document. In other words, each author is associated
> with author-specific Dirichlet distribution over topics, and for a
> particular document, a topic mixture is sampled from that Dirichlet
> distribution. And the inference problem would be to determine the
> topic-word probabilities, and the Dirichlet parameters for each author.
> Does this make sense? Is there existing work of this kind in the
> literature? Would this be interesting? Useful? Tractable?
> Any suggestions or guidance would be really appreciated.
> Thank you,
> Eric
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