Dissertation Information for Wei LiNAME:
DEGREE:
DISCIPLINE:
SCHOOL: ADVISORS: COMMITTEE MEMBERS: MPACT Status: Fully Complete Title: Pachinko allocation: DAG-structured mixture models of topic correlations Abstract: Statistical topic models are increasingly popular tools for summarization and manifold discovery in discrete data. However, the majority of existing approaches capture no or limited correlations between topics. We propose the pachinko allocation model (PAM), which captures arbitrary, nested, and possibly sparse correlations between topics using a directed acyclic graph (DAG). We present various structures within this framework, different parameterizations of topic distributions, and an extension to capture dynamic patterns of topic correlations. We also introduce a non-parametric Bayesian prior to automatically learn the topic structure from data. The model is evaluated on document classification, likelihood of held-out data, the ability to support fine-grained topics, and topical keyword coherence. With a highly-scalable approximation, PAM has also been applied to discover topic hierarchies in very large datasets. |
MPACT Scores for Wei LiA = 0 Advisors and Advisees Graphgenerating graph, please reload |
Students under Wei Li
ADVISEES:
- None
COMMITTEESHIPS:
- None