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...
Web-page classification is much more difficult than pure-text classification due to a large variety of noisy information embedded in Web pages. In this paper, we propose a new Web...
The success of a software project is largely dependent upon the quality of the Software Requirements Specification (SRS) document, which serves as a medium to communicate user req...
Readers on the Web often skim through text to cope with the volume of available information. In a previous study [11] readers’ eye movements were tracked as they skimmed through...
Incorporating semantic features from the WordNet lexical database is among one of the many approaches that have been tried to improve the predictive performance of text classifica...