Paper Info
Title
Analyzing entities and topics in news articles using statistical topic models
Abstract
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.
Year
DOI
Venue
2006
10.1007/11760146_9
ISI
Keywords
Field
DocType
artificial intelligence,computer security,statistical analysis,modeling
Data mining,Latent semantic indexing,Latent Dirichlet allocation,Computer science,Artificial intelligence,Natural language processing,Topic model,Latent semantic analysis,Language model,Statistical analysis
Conference
Volume
ISSN
ISBN
3975
0302-9743
3-540-34478-0
Citations 
PageRank 
References 
19
1.25
13
Authors
4
Name
Order
Citations
PageRank
David Newman1131973.72
Chaitanya Chemudugunta242025.61
Padhraic Smyth371481451.38
Mark Steyvers41980156.87