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» Improving Mention Detection Robustness to Noisy Input
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EMNLP
2010
13 years 2 months ago
Improving Mention Detection Robustness to Noisy Input
Information-extraction (IE) research typically focuses on clean-text inputs. However, an IE engine serving real applications yields many false alarms due to less-well-formed input...
Radu Florian, John F. Pitrelli, Salim Roukos, Imed...
TIP
1998
161views more  TIP 1998»
13 years 4 months ago
Robust anisotropic diffusion
—Relations between anisotropic diffusion and robust statistics are described in this paper. Specifically, we show that anisotropic diffusion can be seen as a robust estimation p...
Michael J. Black, Guillermo Sapiro, David H. Marim...
ISNN
2011
Springer
12 years 7 months ago
Robust Multi-stream Keyword and Non-linguistic Vocalization Detection for Computationally Intelligent Virtual Agents
Abstract. Systems for keyword and non-linguistic vocalization detection in conversational agent applications need to be robust with respect to background noise and different speak...
Martin Wöllmer, Erik Marchi, Stefano Squartin...
CAIP
2003
Springer
236views Image Analysis» more  CAIP 2003»
13 years 10 months ago
Efficient Algorithm of Eye Image Check for Robust Iris Recognition System
For the improvement of iris recognition system performance, the filtering algorithm that picks out counterfeit and noisy data is very important. In this paper, as a part of preproc...
Jain Jang, Kwiju Kim, Yillbyung Lee
VIS
2004
IEEE
214views Visualization» more  VIS 2004»
14 years 6 months ago
Surface Reconstruction of Noisy and Defective Data Sets
We present a novel surface reconstruction algorithm that can recover high-quality surfaces from noisy and defective data sets without any normal or orientation information. A set ...
Hui Xie, Kevin T. McDonnell, Hong Qin