286 search results - page 3 / 58 » Learning to Classify Texts Using Positive and Unlabeled Data |
ICMCS
2005
IEEE
13 years 11 months ago
2005
IEEE
Training a good text detector requires a large amount of labeled data, which can be very expensive to obtain. Cotraining has been shown to be a powerful semi-supervised learning t...
EMNLP
2008
13 years 7 months ago
2008
We present a discriminative method for learning selectional preferences from unlabeled text. Positive examples are taken from observed predicate-argument pairs, while negatives ar...
EMNLP
2010
13 years 3 months ago
2010
In this paper, we introduce a method that automatically builds text classifiers in a new language by training on already labeled data in another language. Our method transfers the...
WAIM
2010
Springer
13 years 10 months ago
2010
Springer
Abstract. The problem of classification from positive and unlabeled examples attracts much attention currently. However, when the number of unlabeled negative examples is very sma...
IJCAI
2007
13 years 6 months ago
2007
Traditional classification involves building a classifier using labeled training examples from a set of predefined classes and then applying the classifier to classify test instan...