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» Learning from Ambiguously Labeled Examples
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AAAI
2012
12 years 1 months ago
Ontological Smoothing for Relation Extraction with Minimal Supervision
Relation extraction, the process of converting natural language text into structured knowledge, is increasingly important. Most successful techniques use supervised machine learni...
Congle Zhang, Raphael Hoffmann, Daniel S. Weld
ICML
2010
IEEE
14 years 1 days ago
Structured Output Learning with Indirect Supervision
We present a novel approach for structure prediction that addresses the difficulty of obtaining labeled structures for training. We observe that structured output problems often h...
Ming-Wei Chang, Vivek Srikumar, Dan Goldwasser, Da...
UAI
2003
14 years 10 days ago
On Information Regularization
We formulate a principle for classification with the knowledge of the marginal distribution over the data points (unlabeled data). The principle is cast in terms of Tikhonov styl...
Adrian Corduneanu, Tommi Jaakkola
KDD
2005
ACM
153views Data Mining» more  KDD 2005»
14 years 11 months ago
Improving discriminative sequential learning with rare--but--important associations
Discriminative sequential learning models like Conditional Random Fields (CRFs) have achieved significant success in several areas such as natural language processing, information...
Xuan Hieu Phan, Minh Le Nguyen, Tu Bao Ho, Susumu ...
ICML
2009
IEEE
14 years 11 months ago
Compositional noisy-logical learning
We describe a new method for learning the conditional probability distribution of a binary-valued variable from labelled training examples. Our proposed Compositional Noisy-Logica...
Alan L. Yuille, Songfeng Zheng