This paper presents a general framework for building classifiers that deal with short and sparse text & Web segments by making the most of hidden topics discovered from larges...
Accurate web page classification often depends crucially on information gained from neighboring pages in the local web graph. Prior work has exploited the class labels of nearby p...
In this paper, we systematically assess the value of using web-scale N-gram data in state-of-the-art supervised NLP classifiers. We compare classifiers that include or exclude fea...
This paper presents a novel method for the classification of images that combines information extracted from the images and contextual information. The main hypothesis is that con...
In this paper, we study the problem of learning block classification models to estimate block functions. We distinguish general models, which are learned across multiple sites, an...