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IJCAI
2003
13 years 5 months ago
Evaluating Classifiers by Means of Test Data with Noisy Labels
Often the most expensive and time-consuming task in building a pattern recognition system is col­ lecting and accurately labeling training and testing data. In this paper, we exp...
Chuck P. Lam, David G. Stork
KDD
2008
ACM
148views Data Mining» more  KDD 2008»
14 years 4 months ago
Get another label? improving data quality and data mining using multiple, noisy labelers
This paper addresses the repeated acquisition of labels for data items when the labeling is imperfect. We examine the improvement (or lack thereof) in data quality via repeated la...
Victor S. Sheng, Foster J. Provost, Panagiotis G. ...
ICDM
2009
IEEE
172views Data Mining» more  ICDM 2009»
13 years 2 months ago
Evaluating Statistical Tests for Within-Network Classifiers of Relational Data
Recently a number of modeling techniques have been developed for data mining and machine learning in relational and network domains where the instances are not independent and ide...
Jennifer Neville, Brian Gallagher, Tina Eliassi-Ra...
NLE
2008
108views more  NLE 2008»
13 years 4 months ago
Using automatically labelled examples to classify rhetorical relations: an assessment
Being able to identify which rhetorical relations (e.g., contrast or explanation) hold between spans of text is important for many natural language processing applications. Using ...
Caroline Sporleder, Alex Lascarides
ACL
2008
13 years 6 months ago
Adapting a WSJ-Trained Parser to Grammatically Noisy Text
We present a robust parser which is trained on a treebank of ungrammatical sentences. The treebank is created automatically by modifying Penn treebank sentences so that they conta...
Jennifer Foster, Joachim Wagner, Josef van Genabit...