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» Painless Labeling with Application to Text Mining
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KDD
2010
ACM
247views Data Mining» more  KDD 2010»
15 years 1 months ago
Active learning for biomedical citation screening
Active learning (AL) is an increasingly popular strategy for mitigating the amount of labeled data required to train classifiers, thereby reducing annotator effort. We describe ...
Byron C. Wallace, Kevin Small, Carla E. Brodley, T...
ICDM
2009
IEEE
117views Data Mining» more  ICDM 2009»
15 years 5 months ago
Discriminative Mixed-Membership Models
Although mixed-membership models have achieved great success in unsupervised learning, they have not been widely applied to classification problems. In this paper, we propose a f...
Hanhuai Shan, Arindam Banerjee, Nikunj C. Oza
ICDM
2008
IEEE
136views Data Mining» more  ICDM 2008»
15 years 5 months ago
Document-Word Co-regularization for Semi-supervised Sentiment Analysis
The goal of sentiment prediction is to automatically identify whether a given piece of text expresses positive or negative opinion towards a topic of interest. One can pose sentim...
Vikas Sindhwani, Prem Melville
WSDM
2012
ACM
296views Data Mining» more  WSDM 2012»
13 years 6 months ago
Inferring social ties across heterogenous networks
It is well known that different types of social ties have essentially different influence between people. However, users in online social networks rarely categorize their contact...
Jie Tang, Tiancheng Lou, Jon M. Kleinberg
ROCAI
2004
Springer
15 years 4 months ago
Learning Interestingness Measures in Terminology Extraction. A ROC-based approach
Abstract. In the field of Text Mining, a key phase in data preparation is concerned with the extraction of terms, i.e. collocation of words attached to specific concepts (e.g. Ph...
Mathieu Roche, Jérôme Azé, Yve...