An Algorithm that Learns What‘s in a Name
Machine Learning - Special issue on natural language learning
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Topic Detection and Tracking: Event-Based Information Organization
Topic Detection and Tracking: Event-Based Information Organization
Modern Information Retrieval
The Journal of Machine Learning Research
Statistical entity-topic models
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
On GMAP: and other transformations
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Context sensitive topic models for author influence in document networks
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
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Conveying information about who, what, when and where is a primary purpose of some genres of documents, typically news articles. Statistical models that capture dependencies between named entities and topics can play an important role. Although some relationships between who and where should be mentioned in such a document, no statistical topic models explicitly address in handling such information the textual interactions between a who-entity and a where-entity. This paper presents a statistical model that directly captures the dependencies between an arbitrary number of word types, such as who-entities, where-entities and topics, mentioned in each document. We show that this multitype topic model performs better at making predictions on entity networks, in which each vertex represents an entity and each edge weight represents how a pair of entities at the incident vertices is closely related, through our experiments on predictions of who-entities and links between them. We also demonstrate the scale-free property in the weighted networks of entities extracted from written mentions.