[Topic-models] On author topic model

Eric Kang erickangnz at gmail.com
Sat Jul 22 12:37:52 EDT 2017


Thanks, Prof. Buntine. I'll read your paper and the literature it references. 

A quick question though: when you say avoid HCRP, why is that? Is that because of slow convergence, or something else?

Regards,
Eric


> On Jul 20, 2017, at 8:41 PM, Wray Buntine <wray.buntine at monash.edu> wrote:
> 
> Hi
> 
> 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
> http://topicmodels.org
> 
>> 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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> 
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