The main problems in text classification are lack of labeled data, as well as the cost of labeling the unlabeled data. We address these problems by exploring co-training - an algo...
We investigate the impact of time on the predictability of sentiment classification research for models created from web logs. We show that sentiment classifiers are time dependen...
Abstract. In this paper, we test some of the most commonly used classifiers to identify which ones are the most robust to changing environments. The environment may change over tim...
Houman Abbasian, Chris Drummond, Nathalie Japkowic...
In many important text classification problems, acquiring class labels for training documents is costly, while gathering large quantities of unlabeled data is cheap. This paper sh...
Kamal Nigam, Andrew McCallum, Sebastian Thrun, Tom...
The use of Bayesian networks for classification problems has received significant recent attention. Although computationally efficient, the standard maximum likelihood learning me...