Paper: SSHLDA: A Semi-Supervised Hierarchical Topic Model

ACL ID D12-1073
Title SSHLDA: A Semi-Supervised Hierarchical Topic Model
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2012

Supervised hierarchical topic modeling and unsupervised hierarchical topic modeling are usually used to obtain hierarchical topics, such as hLLDA and hLDA. Supervised hierarchi- cal topic modeling makes heavy use of the in- formation from observed hierarchical labels, but cannot explore new topics; while unsu- pervised hierarchical topic modeling is able to detect automatically new topics in the data space, but does not make use of any informa- tion from hierarchical labels. In this paper, we propose a semi-supervised hierarchical topic model which aims to explore new topics auto- matically in the data space while incorporating the information from observed hierarchical la- bels into the modeling process, called Semi- Supervised Hierarchical Latent Dirichlet Al- location (SSHLDA). We also ...