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» Detecting Topic Drift with Compound Topic Models
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HICSS
2010
IEEE
258views Biometrics» more  HICSS 2010»
13 years 7 months ago
An Empirical Comparison of Four Text Mining Methods
The amount of textual data that is available for researchers and businesses to analyze is increasing at a dramatic rate. This reality has led IS researchers to investigate various...
Sangno Lee, Jeff Baker, Jaeki Song, James C. Wethe...
NIPS
2004
13 years 8 months ago
Outlier Detection with One-class Kernel Fisher Discriminants
The problem of detecting "atypical objects" or "outliers" is one of the classical topics in (robust) statistics. Recently, it has been proposed to address this...
Volker Roth
COLING
2010
13 years 2 months ago
Modeling Socio-Cultural Phenomena in Discourse
In this paper, we describe a novel approach to computational modeling and understanding of social and cultural phenomena in multi-party dialogues. We developed a two-tier approach...
Tomek Strzalkowski, George Aaron Broadwell, Jennif...
CVPR
2008
IEEE
14 years 9 months ago
Trajectory analysis and semantic region modeling using a nonparametric Bayesian model
We propose a novel nonparametric Bayesian model, Dual Hierarchical Dirichlet Processes (Dual-HDP), for trajectory analysis and semantic region modeling in surveillance settings, i...
Xiaogang Wang, Keng Teck Ma, Gee Wah Ng, W. Eric L...
SIGIR
2004
ACM
14 years 24 days ago
Focused named entity recognition using machine learning
In this paper we study the problem of finding most topical named entities among all entities in a document, which we refer to as focused named entity recognition. We show that th...
Li Zhang, Yue Pan, Tong Zhang