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ICDM
2009
IEEE
233views Data Mining» more  ICDM 2009»
13 years 11 months ago
Semi-Supervised Sequence Labeling with Self-Learned Features
—Typical information extraction (IE) systems can be seen as tasks assigning labels to words in a natural language sequence. The performance is restricted by the availability of l...
Yanjun Qi, Pavel Kuksa, Ronan Collobert, Kunihiko ...
IJCAI
2003
13 years 5 months ago
Semi-Supervised Learning with Explicit Misclassification Modeling
This paper investigates a new approach for training discriminant classifiers when only a small set of labeled data is available together with a large set of unlabeled data. This a...
Massih-Reza Amini, Patrick Gallinari
KDD
2008
ACM
140views Data Mining» more  KDD 2008»
14 years 4 months ago
Semi-supervised approach to rapid and reliable labeling of large data sets
Supervised classification methods have been shown to be very effective for a large number of applications. They require a training data set whose instances are labeled to indicate...
György J. Simon, Vipin Kumar, Zhi-Li Zhang
WACV
2005
IEEE
13 years 10 months ago
Semi-Supervised Self-Training of Object Detection Models
The construction of appearance-based object detection systems is time-consuming and difficult because a large number of training examples must be collected and manually labeled i...
Chuck Rosenberg, Martial Hebert, Henry Schneiderma...
ACL
2011
12 years 8 months ago
Semi-Supervised Frame-Semantic Parsing for Unknown Predicates
We describe a new approach to disambiguating semantic frames evoked by lexical predicates previously unseen in a lexicon or annotated data. Our approach makes use of large amounts...
Dipanjan Das, Noah A. Smith