TY - GEN
T1 - Event detection with spatial latent Dirichlet allocation
AU - Pan, Chi Chun
AU - Mitra, Prasenjit
PY - 2011
Y1 - 2011
N2 - A large number of news articles are generated every day on the Web. Automatically identifying events from a large document collection is a challenging problem. In this paper, we propose two event detection approaches using generative models. We combine the popular LDA model with temporal segmentation and spatial clustering. In addition, we adapt an image segmentation model, SLDA, for spatial-temporal event detection on text. The results of our experiments show that both approaches outperform the traditional content-based clustering approaches on our datasets.
AB - A large number of news articles are generated every day on the Web. Automatically identifying events from a large document collection is a challenging problem. In this paper, we propose two event detection approaches using generative models. We combine the popular LDA model with temporal segmentation and spatial clustering. In addition, we adapt an image segmentation model, SLDA, for spatial-temporal event detection on text. The results of our experiments show that both approaches outperform the traditional content-based clustering approaches on our datasets.
UR - http://www.scopus.com/inward/record.url?scp=79960534133&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79960534133&partnerID=8YFLogxK
U2 - 10.1145/1998076.1998141
DO - 10.1145/1998076.1998141
M3 - Conference contribution
AN - SCOPUS:79960534133
SN - 9781450307444
T3 - Proceedings of the ACM/IEEE Joint Conference on Digital Libraries
SP - 349
EP - 358
BT - JCDL'11 - Proceedings of the 2011 ACM/IEEE Joint Conference on Digital Libraries
T2 - 11th Annual International ACM/IEEE Joint Conference on Digital Libraries, JCDL'11
Y2 - 13 June 2011 through 17 June 2011
ER -