The Journal of Machine Learning Research
HTM: a topic model for hypertexts
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Partially labeled topic models for interpretable text mining
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Semantic topic models: combining word distributional statistics and dictionary definitions
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Mining contentions from discussions and debates
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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Most approaches to topic modeling assume an independence between documents that is frequently violated. We present an topic model that makes use of one or more user-specified graphs describing relationships between documents. These graph are encoded in the form of a Markov random field over topics and serve to encourage related documents to have similar topic structures. Experiments on show upwards of a 10% improvement in modeling performance.