document: 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 .
doc_bio_tags: O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O