The Journal of Machine Learning Research
Extracting relations from large text collections
Extracting relations from large text collections
Mining hidden community in heterogeneous social networks
Proceedings of the 3rd international workshop on Link discovery
POLYPHONET: an advanced social network extraction system from the web
Proceedings of the 15th international conference on World Wide Web
Pachinko allocation: DAG-structured mixture models of topic correlations
ICML '06 Proceedings of the 23rd international conference on Machine learning
Discovering groups of people in Google news
Proceedings of the 1st ACM international workshop on Human-centered multimedia
Social Network Extraction of Academic Researchers
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on 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
Social influence analysis in large-scale networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Relational learning via latent social dimensions
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Topic and role discovery in social networks with experiments on enron and academic email
Journal of Artificial Intelligence Research
Flink: Semantic Web technology for the extraction and analysis of social networks
Web Semantics: Science, Services and Agents on the World Wide Web
Relational duality: unsupervised extraction of semantic relations between entities on the web
Proceedings of the 19th international conference on World wide web
Modeling relationship strength in online social networks
Proceedings of the 19th international conference on World wide web
Strength of social influence in trust networks in product review sites
Proceedings of the fourth ACM international conference on Web search and data mining
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Extracting relations among different entities from various data sources has been an important topic in data mining. While many methods focus only on a single type of relations, real world entities maintain relations that contain much richer information. We propose a hierarchical Bayesian model for extracting multi-dimensional relations among entities from a text corpus. Using data from Wikipedia, we show that our model can accurately predict the relevance of an entity given the topic of the document as well as the set of entities that are already mentioned in that document.