Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
A Hierarchical Model for Clustering and Categorising Documents
Proceedings of the 24th BCS-IRSG European Colloquium on IR Research: Advances in Information Retrieval
Measuring Similarity between Ontologies
EKAW '02 Proceedings of the 13th International Conference on Knowledge Engineering and Knowledge Management. Ontologies and the Semantic Web
The Journal of Machine Learning Research
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
Evaluation methods for topic models
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Determining Automatically the Size of Learned Ontologies
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Estimating Likelihoods for Topic Models
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
Gold Standard Evaluation of Ontology Learning Methods through Ontology Transformation and Alignment
IEEE Transactions on Knowledge and Data Engineering
On the use of consensus clustering for incremental learning of topic hierarchies
SBIA'12 Proceedings of the 21st Brazilian conference on Advances in Artificial Intelligence
A hierarchical Dirichlet model for taxonomy expansion for search engines
Proceedings of the 23rd international conference on World wide web
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This paper presents hHDP, a hierarchical algorithm for representing a document collection as a hierarchy of latent topics, based on Dirichlet process priors. The hierarchical nature of the algorithm refers to the Bayesian hierarchy that it comprises, as well as to the hierarchy of the latent topics. hHDP relies on nonparametric Bayesian priors and it is able to infer a hierarchy of topics, without making any assumption about the depth of the learned hierarchy and the branching factor at each level. We evaluate the proposed method on real-world data sets in document modeling, as well as in ontology learning, and provide qualitative and quantitative evaluation results, showing that the model is robust, it models accurately the training data set and is able to generalize on held-out data.