Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
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
A probabilistic approach to spatiotemporal theme pattern mining on weblogs
Proceedings of the 15th international conference on World Wide Web
ICML '06 Proceedings of the 23rd international conference on Machine learning
Statistical entity-topic models
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Connections between the lines: augmenting social networks with text
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Sequential Latent Dirichlet Allocation: Discover Underlying Topic Structures within a Document
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
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Topic mining is regarded as a powerful method to analyze documents, and topic models are used to annotate relationships or to get a topic flow. The research aim in this paper is to get topic flows of entities and entity groups within one document. We propose two topic models: Entity Group Topic Model (EGTM) and Sequential Entity Group Topic Model (S-EGTM). These models provide two contributions. First, topic distributions of entities and entity groups can be analyzed. Second, the topic flow of each entity or each entity group can be captured, through segments in one document. We develop collapsed gibbs sampling methods for performing approximate inference of the models. By experiments, we demonstrate the models by showing the analysis of topics, prediction performance, and the topic flows over segments in one document.