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» Class Noise and Supervised Learning in Medical Domains: The ...
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CBMS
2006
IEEE
13 years 10 months ago
Class Noise and Supervised Learning in Medical Domains: The Effect of Feature Extraction
Inductive learning systems have been successfully applied in a number of medical domains. It is generally accepted that the highest accuracy results that an inductive learning sys...
Mykola Pechenizkiy, Alexey Tsymbal, Seppo Puuronen...
ACL
2010
13 years 2 months ago
Experiments in Graph-Based Semi-Supervised Learning Methods for Class-Instance Acquisition
Graph-based semi-supervised learning (SSL) algorithms have been successfully used to extract class-instance pairs from large unstructured and structured text collections. However,...
Partha Pratim Talukdar, Fernando Pereira
BMCBI
2008
185views more  BMCBI 2008»
13 years 4 months ago
Mining clinical relationships from patient narratives
Background: The Clinical E-Science Framework (CLEF) project has built a system to extract clinically significant information from the textual component of medical records in order...
Angus Roberts, Robert J. Gaizauskas, Mark Hepple, ...
LREC
2008
193views Education» more  LREC 2008»
13 years 5 months ago
Eksairesis: A Domain-Adaptable System for Ontology Building from Unstructured Text
This paper describes Eksairesis, a system for learning economic domain knowledge automatically from Modern Greek text. The knowledge is in the form of economic terms and the seman...
Katia Kermanidis, Aristomenis Thanopoulos, Manolis...
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 ...