Research on Key Factors in Multi-document Topic Modeling Application with HLDA
Heng We · Zhongwen xinxi xuebao · 2013
The results of hLDA(hierarchical Latent Dirichlet Allocation)in the hierarchical topic modeling have been widely validated.In order to achieve semi-supervised or unsupervised learning,cross-validation or sampling super parameters are usually used to determine the true parameters.However,corpus features,modeling demand and some other factors are uncertain.Hence,parameter adjustment,modeling effectiveness and efficiency are difficulty to achieve in practical applications.This paper builds a unified analytical framework by combining Bayesian theory and boundary information,analyzes the key factors in its topic modeling,then gives a series of practical and effective modeling strategies and processes,and finally evaluates the modeling results with multi-document summary corpus from ACL MultiLing 2013.