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KDD
2009
ACM
227views Data Mining» more  KDD 2009»
14 years 6 months ago
Efficiently learning the accuracy of labeling sources for selective sampling
Many scalable data mining tasks rely on active learning to provide the most useful accurately labeled instances. However, what if there are multiple labeling sources (`oracles...
Pinar Donmez, Jaime G. Carbonell, Jeff Schneider
JCDL
2011
ACM
226views Education» more  JCDL 2011»
12 years 8 months ago
Measuring historical word sense variation
We describe here a method for automatically identifying word sense variation in a dated collection of historical books in a large digital library. By leveraging a small set of kno...
David Bamman, Gregory Crane
EMNLP
2010
13 years 3 months ago
Negative Training Data Can be Harmful to Text Classification
This paper studies the effects of training data on binary text classification and postulates that negative training data is not needed and may even be harmful for the task. Tradit...
Xiaoli Li, Bing Liu, See-Kiong Ng
ICTIR
2009
Springer
13 years 3 months ago
Training Data Cleaning for Text Classification
Abstract. In text classification (TC) and other tasks involving supervised learning, labelled data may be scarce or expensive to obtain; strategies are thus needed for maximizing t...
Andrea Esuli, Fabrizio Sebastiani
WSDM
2012
ACM
207views Data Mining» more  WSDM 2012»
12 years 1 months ago
Sequence clustering and labeling for unsupervised query intent discovery
One popular form of semantic search observed in several modern search engines is to recognize query patterns that trigger instant answers or domain-specific search, producing sem...
Jackie Chi Kit Cheung, Xiao Li