The Journal of Machine Learning Research
Statistical Debugging Using Latent Topic Models
ECML '07 Proceedings of the 18th European conference on Machine Learning
Incorporating domain knowledge into topic modeling via Dirichlet Forest priors
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Latent Dirichlet Allocation with topic-in-set knowledge
SemiSupLearn '09 Proceedings of the NAACL HLT 2009 Workshop on Semi-Supervised Learning for Natural Language Processing
Automatic evaluation of topic coherence
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
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This paper investigates the relation between prior knowledge and latent topic classification. There are many cases where the topic classification done by Latent Dirichlet Allocation results in the different classification that humans expect. To improve this problem, several studies using Dirichlet Forest prior instead of Dirichlet distribution have been studied in order to provide constraints on words so as they are classified into the same or not the same topics. However, in many cases, the prior knowledge is constructed from a subjective view of humans, but is not constructed based on the properties of target documents. In this study, we construct prior knowledge based on the words extracted from target documents and provide it as constraints for topic classification. We discuss the result of topic classification with the constraints.