Many methods, including supervised and unsupervised algorithms, have been developed for extractive document summarization. Most supervised methods consider the summarization task ...
The importance of text mining stems from the availability of huge volumes of text databases holding a wealth of valuable information that needs to be mined. Text categorization is...
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...
It is difficult to apply machine learning to new domains because often we lack labeled problem instances. In this paper, we provide a solution to this problem that leverages domai...
Document classification is a key task for many text mining applications. However, traditional text classification requires labeled data to construct reliable and accurate classifie...