期刊名称：Conference on European Chapter of the Association for Computational Linguistics (EACL)
出版年度：2012
卷号：2012
出版社：ACL Anthology
摘要：Topic modeling with a tree-based prior has
been used for a variety of applications because
it can encode correlations between words
that traditional topic modeling cannot. However,
its expressive power comes at the cost
of more complicated inference. We extend
the SPARSELDA (Yao et al., 2009) inference
scheme for latent Dirichlet allocation (LDA)
to tree-based topic models. This sampling
scheme computes the exact conditional distribution
for Gibbs sampling much more quickly
than enumerating all possible latent variable
assignments. We further improve performance
by iteratively refining the sampling distribution
only when needed. Experiments show that the
proposed techniques dramatically improve the
computation time.
