Statistical entity-topic models

The primary purpose of news articles is to convey information about who, what, when and where. But learning and summarizing these relationships for collections of thousands to millions of articles is difficult. While statistical topic models have been highly successful at topically summarizing huge collections of text documents, they do not explicitly address the textual interactions between who/where, i.e. named entities (persons, organizations, locations) and what, i.e. the topics. We present new graphical models that directly learn the relationship between topics discussed in news articles and entities mentioned in each article. We show how these entity-topic models, through a better understanding of the entity-topic relationships, are better at making predictions about entities. Categories and Subject Descriptors G.3 [Probability and Statistics]: probabilistic algorithms; H.4 [Information Systems Applications]: Miscellaneous General Terms Algorithms Keywords Text Modeling, Topic M...

• Type: Conference
• Year: 2006
• Where: KDD
• Authors: David Newman, Chaitanya Chemudugunta, Padhraic Smyth
• Keywords: Data Mining, KDD 2006, Statistical Topic Models, Terms Algorithms Keywords, Topic Modeling