Hierarchical document categorization with support vector machines
Proceedings of the thirteenth ACM international conference on Information and knowledge management
The nested chinese restaurant process and bayesian nonparametric inference of topic hierarchies
Journal of the ACM (JACM)
Latent variable models of concept-attribute attachment
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
Coupled semi-supervised learning for information extraction
Proceedings of the third ACM international conference on Web search and data mining
The Bayesian structural EM algorithm
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Discovering relations between noun categories
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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Exponential growth of unlabeled web-scale datasets, and class hierarchies to represent them, has given rise to new challenges for hierarchical classification. It is costly and time consuming to create a complete ontology of classes to represent entities on the Web. Hence, there is a need for techniques that can do hierarchical classification of entities into incomplete ontologies. In this paper we present Hierarchical Exploratory EM algorithm (an extension of the Exploratory EM algorithm [7]) that takes a seed class hierarchy and seed class instances as input. Our method classifies relevant entities into some of the classes from the seed hierarchy and on its way adds newly discovered classes into the hierarchy. Experiments with subsets of the NELL ontology and text datasets derived from the ClueWeb09 corpus show that our Hierarchical Exploratory EM approach improves seed class F1 by up to 21% when compared to its semi-supervised counterpart.