Analyzing Entities and Topics in News Articles Using Statistical Topic Models

  • David Newman
  • Chaitanya Chemudugunta
  • Padhraic Smyth
  • Mark Steyvers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3975)


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.


Topic Model Latent Dirichlet Allocation News Article Latent Semantic Analysis Latent Semantic Indexing 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • David Newman
    • 1
  • Chaitanya Chemudugunta
    • 1
  • Padhraic Smyth
    • 1
  • Mark Steyvers
    • 2
  1. 1.Department of Computer ScienceUC IrvineIrvine
  2. 2.Department of Cognitive ScienceUC IrvineIrvine

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