no code implementations • 22 Oct 2021 • Jinjin Chi, Zhiyao Yang, Jihong Ouyang, Ximing Li
The basic idea is to introduce a variational distribution as the approximation of the true continuous barycenter, so as to frame the barycenters computation problem as an optimization problem, where parameters of the variational distribution adjust the proxy distribution to be similar to the barycenter.
no code implementations • 23 Oct 2018 • Jinjin Chi, Jihong Ouyang, Changchun Li, Xueyang Dong, Xi-Ming Li, Xinhua Wang
The top word list, i. e., the top-M words with highest marginal probability in a given topic, is the standard topic representation in topic models.
no code implementations • COLING 2016 • Xi-Ming Li, Jinjin Chi, Changchun Li, Jihong Ouyang, Bo Fu
Gaussian LDA integrates topic modeling with word embeddings by replacing discrete topic distribution over word types with multivariate Gaussian distribution on the embedding space.