Event detection with spatial latent Dirichlet allocation
C. Pan, and P. Mitra. Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries, page 349--358. New York, NY, USA, ACM, (2011)
DOI: 10.1145/1998076.1998141
Abstract
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.
Description
Event detection with spatial latent Dirichlet allocation
%0 Conference Paper
%1 Pan:2011:EDS:1998076.1998141
%A Pan, Chi-Chun
%A Mitra, Prasenjit
%B Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries
%C New York, NY, USA
%D 2011
%I ACM
%K detection event lda pub spatial
%P 349--358
%R 10.1145/1998076.1998141
%T Event detection with spatial latent Dirichlet allocation
%U http://doi.acm.org/10.1145/1998076.1998141
%X 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.
%@ 978-1-4503-0744-4
@inproceedings{Pan:2011:EDS:1998076.1998141,
abstract = {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.},
acmid = {1998141},
added-at = {2013-05-24T11:08:27.000+0200},
address = {New York, NY, USA},
author = {Pan, Chi-Chun and Mitra, Prasenjit},
biburl = {https://www.bibsonomy.org/bibtex/21b27e2befe837ed3ade3e1b11f205596/schwemmlein},
booktitle = {Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries},
description = {Event detection with spatial latent Dirichlet allocation},
doi = {10.1145/1998076.1998141},
interhash = {50f73bf4b179df591b5e61893df575d3},
intrahash = {1b27e2befe837ed3ade3e1b11f205596},
isbn = {978-1-4503-0744-4},
keywords = {detection event lda pub spatial},
location = {Ottawa, Ontario, Canada},
numpages = {10},
pages = {349--358},
publisher = {ACM},
series = {JCDL '11},
timestamp = {2013-05-24T11:08:27.000+0200},
title = {Event detection with spatial latent Dirichlet allocation},
url = {http://doi.acm.org/10.1145/1998076.1998141},
year = 2011
}