Neural Embedding Allocation:Distributed Representations of Topic Models
Figure 8: Randomly selected topic pairs from LDA and NEA, with LDA trained on the NIPS corpus for K=2,000.
['LDA bayesian prior bayes posterior framework priors likelihood bars note compute', 'NEA bayesian bayes posterior priors likelihood prior framework note probability bars', 'LDA images image recognition vision pixel techniques pixels visual computed applied', 'NEA images image visual recognition pixels pixel illumination intensity pairs matching', 'LDA phrase sentences clause structure sentence phrases syntactic connectionist tolerance previous', 'NEA sentences phrase structure sentence clause activation connectionist phrases roles agent', 'LDA regression linear ridge quadratic squared nonparametric dimensionality variables smoothing friedman', 'NEA regression linear ridge quadratic variables nonparametric squared multivariate kernel basis']
In this section, we demonstrate our results on other datasets by repeating the similar experiments on them. First, we found that most of the topics produced by both models are interpretable, and NEA was able to approximately recover the original LDA’s topics.