By Topic

Margin-based active learning and background knowledge in text mining

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Silva, C. ; Departamento Eng. Informatica, Coimbra Univ., Portugal ; Ribeiro, B.

Text mining, also known as intelligent text analysis, text data mining or knowledge-discovery in text, refers generally to the process of extracting interesting and nontrivial information and knowledge from text. One of the main problems with text mining and classification systems is the lack of labeled data, as well as the cost of labeling unlabeled data (Kiritchenko and Matwin 2001). Thus, there is a growing interest in exploring the use of unlabeled data as a way to improve classification performance in text classification. The ready availability of this kind of data in most applications makes it an appealing source of information. In this work we evaluate the benefits of introducing unlabeled data in a support vector machine automatic text classifier. We further evaluate the possibility of learning actively and propose a method for choosing the samples to be learned.

Published in:

Hybrid Intelligent Systems, 2004. HIS '04. Fourth International Conference on

Date of Conference:

5-8 Dec. 2004