[Topic-models] On author topic model

Thibaut Thonet thibaut.thonet at irit.fr
Sun Jul 23 18:11:23 EDT 2017


Hi Eric,

You can also have a look at the following papers:

  * Yang, M., & Hsu, W. H. (2016). HDPauthor: A New Hybrid Author-Topic
    Model using Latent Dirichlet Allocation and Hierarchical Dirichlet
    Processes. In Proceedings of the 25th International Conference
    Companion on World Wide Web (pp. 619–624).
    http://doi.org/10.1145/2872518.2890561
  * Xuan, J., Lu, J., Zhang, G., Xu, R. Y. Da, & Luo, X. (2015).
    Infinite Author Topic Model based on Mixed Gamma-Negative Binomial
    Process. In Proceedings of the 2015 IEEE International Conference on
    Data Mining (pp. 489–498). http://doi.org/10.1109/ICDM.2015.19

They both propose non-parametric author-topic models with hierarchical 
priors, similar to what you described -- document-level topic 
distributions' prior is based on author-specific topic distributions.

Best,

Thibaut

Le 21/07/2017 à 02:20, Eric Kang a écrit :
> 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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