NDLI: Short comings of latent models in supervised settings

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Short comings of latent models in supervised settings

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Short comings of latent models in supervised settings

Content Provider	ACM Digital Library
Author	Krishnan, Vijay
Abstract	The Aspect Model [1, 2] and the Latent Dirichlet Allocation Model [3, 4] are latent generative models proposed with the objective of modeling discrete data such as text. Though it is not explicitly published (to the best of our knowledge), it is reasonably well known in there search community that the Aspect Model does not perform very well in supervised settings and also that latent models are frequently not identifiable, i.e. their optimal parameters are not unique.In this paper, we make a much stronger claim about the pitfalls of commonly-used latent models. By constructing a small, synthetic, but by no means unrealistic corpus, we show that latent models have inherent limitations that prevent them from recovering semantically meaningful parameters from data generated from a reasonable generative distribution. In fact, our experiments with supervised classification using the Aspect Model, showed that its performance was rather poor, even worse than Naive Bayes, leading us to the synthetic study.We also analyze the scenario of using tempered EM and show that it would not plug the above shortcomings. Our analysis suggests that there is also some scope for improvement in the Latent Dirichlet Allocation Model(LDA) [3, 4]. We then use our insight into the shortcomings of these models, to come up with a promising variant of the LDA, that does not suffer from the aforesaid drawbacks. This could potentially lead to much better performance and model fit, in the supervised scenario.
Starting Page	625
Ending Page	626
Page Count	2
File Format	PDF
ISBN	1595930345
DOI	10.1145/1076034.1076160
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2005-08-15
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Corpus modeling Latent models
Content Type	Text
Resource Type	Article

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