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ACL
2010
13 years 5 months ago
Creating Robust Supervised Classifiers via Web-Scale N-Gram Data
In this paper, we systematically assess the value of using web-scale N-gram data in state-of-the-art supervised NLP classifiers. We compare classifiers that include or exclude fea...
Shane Bergsma, Emily Pitler, Dekang Lin
ICASSP
2009
IEEE
14 years 2 months ago
Language model transformation applied to lightly supervised training of acoustic model for congress meetings
For effective training of acoustic and language models for spontaneous speech such as meetings, it is significant to exploit the texts available in a large scale, which may not b...
Tatsuya Kawahara, Masato Mimura, Yuka Akita
ICRA
2008
IEEE
141views Robotics» more  ICRA 2008»
14 years 2 months ago
Tracking interacting targets with laser scanner via on-line supervised learning
— Successful multi-target tracking requires locating the targets and labeling their identities. For the laser based tracking system, the latter becomes significantly more challen...
Xuan Song, Jinshi Cui, Xulei Wang, Huijing Zhao, H...
EMNLP
2007
13 years 9 months ago
Semi-Supervised Structured Output Learning Based on a Hybrid Generative and Discriminative Approach
This paper proposes a framework for semi-supervised structured output learning (SOL), specifically for sequence labeling, based on a hybrid generative and discriminative approach...
Jun Suzuki, Akinori Fujino, Hideki Isozaki
NIPS
2007
13 years 9 months ago
Semi-Supervised Multitask Learning
A semi-supervised multitask learning (MTL) framework is presented, in which M parameterized semi-supervised classifiers, each associated with one of M partially labeled data mani...
Qiuhua Liu, Xuejun Liao, Lawrence Carin