Multi-Entity-Topic Models with Who-entities and Where-entities

抄録

Conveying information about who, what, when and where is a primary purpose of news articles. To handle such information, statistical models that capture dependencies between named entities and topics can serve an important role. Although some relationships between who and where should be mentioned in a news story, no topic models explicitly addressed the textual interactions between a who-entity anda where-entity. This paper presents a new statistical model that directly captures dependencies between topics, who-entities and where-entities mentioned in each article. We show, through our experiments, how this multi-entity-topic model performs better at making predictions on who-entities.