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ACL
2006
13 years 9 months ago
Semi-Supervised Conditional Random Fields for Improved Sequence Segmentation and Labeling
We present a new semi-supervised training procedure for conditional random fields (CRFs) that can be used to train sequence segmentors and labelers from a combination of labeled a...
Feng Jiao, Shaojun Wang, Chi-Hoon Lee, Russell Gre...
ICML
2010
IEEE
13 years 8 months ago
Label Ranking under Ambiguous Supervision for Learning Semantic Correspondences
This paper studies the problem of learning from ambiguous supervision, focusing on the task of learning semantic correspondences. A learning problem is said to be ambiguously supe...
Antoine Bordes, Nicolas Usunier, Jason Weston
ACL
2009
13 years 5 months ago
Semi-Supervised Active Learning for Sequence Labeling
While Active Learning (AL) has already been shown to markedly reduce the annotation efforts for many sequence labeling tasks compared to random selection, AL remains unconcerned a...
Katrin Tomanek, Udo Hahn
NIPS
2004
13 years 9 months ago
A Method for Inferring Label Sampling Mechanisms in Semi-Supervised Learning
We consider the situation in semi-supervised learning, where the "label sampling" mechanism stochastically depends on the true response (as well as potentially on the fe...
Saharon Rosset, Ji Zhu, Hui Zou, Trevor Hastie
PKDD
2010
Springer
143views Data Mining» more  PKDD 2010»
13 years 5 months ago
A Unified Approach to Active Dual Supervision for Labeling Features and Examples
Abstract. When faced with the task of building accurate classifiers, active learning is often a beneficial tool for minimizing the requisite costs of human annotation. Traditional ...
Josh Attenberg, Prem Melville, Foster J. Provost