This paper shows that the accuracy of learned text classifiers can be improved by augmenting a small number of labeled training documents with a large pool of unlabeled documents. ...
Kamal Nigam, Andrew McCallum, Sebastian Thrun, Tom...
In many NLP systems, there is a unidirectional flow of information in which a parser supplies input to a semantic role labeler. In this paper, we build a system that allows inform...
Computational models of grounded language learning have been based on the premise that words and concepts are learned simultaneously. Given the mounting cognitive evidence for conc...
In this paper we describe an interactive labeling algorithm, which allows to integrate internal 3D labels into medical visualizations generated from volumetric data sets. The prop...
Social bookmarking has emerged as a growing source of human generated content on the web. In essence, bookmarking involves URLs and tags on them. In this paper, we perform a large...