Previous work using topic model for statistical machine translation (SMT) explore topic information at the word level. However, SMT has been advanced from word-based paradigm to p...
Xinyan Xiao, Deyi Xiong, Min Zhang, Qun Liu, Shoux...
We present a method for unsupervised topic modelling which adapts methods used in document classification (Blei et al., 2003; Griffiths and Steyvers, 2004) to unsegmented multi-pa...
We introduce the Spherical Admixture Model (SAM), a Bayesian topic model for arbitrary 2 normalized data. SAM maintains the same hierarchical structure as Latent Dirichlet Allocat...
Joseph Reisinger, Austin Waters, Bryan Silverthorn...
This work presents a study to bridge topic modeling and personalized search. A probabilistic topic model is used to extract topics from user search history. These topics can be se...
We develop latent Dirichlet allocation with WORDNET (LDAWN), an unsupervised probabilistic topic model that includes word sense as a hidden variable. We develop a probabilistic po...