Analyzing Entities and Topics in News Articles Using Statistical Topic Models

Statistical language models can learn relationships between topics discussed in a document collection and persons, organizations and places mentioned in each document. We present a novel combination of statistical topic models and named-entity recognizers to jointly analyze entities mentioned (persons, organizations and places) and topics discussed in a collection of 330,000 New York Times news articles. We demonstrate an analytic framework which automatically extracts from a large collection: topics; topic trends; and topics that relate entities.

• Type: Journal
• Year: 2006
• Where: ISI
• Authors: David Newman, Chaitanya Chemudugunta, Padhraic Smyth, Mark Steyvers
• Keywords: Document Collection, ISI 2006, Security Privacy, Statistical Language Models, Statistical Topic Models