The Journal of Machine Learning Research
The author-topic model for authors and documents
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Pachinko allocation: DAG-structured mixture models of topic correlations
ICML '06 Proceedings of the 23rd international conference on Machine learning
Mixtures of hierarchical topics with Pachinko allocation
Proceedings of the 24th international conference on Machine learning
Introduction to Information Retrieval
Introduction to Information Retrieval
Combining concept hierarchies and statistical topic models
Proceedings of the 17th ACM conference on Information and knowledge management
Modeling Documents by Combining Semantic Concepts with Unsupervised Statistical Learning
ISWC '08 Proceedings of the 7th International Conference on The Semantic Web
Proceedings of the Second ACM International Conference on Web Search and Data Mining
A hierarchical model of web summaries
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Partially labeled topic models for interpretable text mining
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Statistical topic models for multi-label document classification
Machine Learning
SSHLDA: a semi-supervised hierarchical topic model
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
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Lots of document collections are well organized in hierarchical structure, and such structure can help users browse and understand these collections. Meanwhile, there are a large number of plain document collections loosely organized, and it is difficult for users to understand them effectively. In this paper we study how to automatically integrate latent topics in a plain collection with the topics in a hierarchical structured collection. We propose to use semi-supervised topic modeling to solve the problem in a principled way. The experiments show that the proposed method can generate both meaningful latent topics and expand high quality hierarchical topic structures.