Text Classification from Partially Labeled Distributed Data



One of the main problems with text classification systems is the lack of labeled data, as well as the cost of labeling unlabeled data. Thus, there is a growing interest in exploring the combination of labeled and unlabeled data, i.e., partially labeled 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.

The distributed nature of the data, usually available online, makes it a very interesting problem suited to be solved with distributed computing tools, delivered by emerging GRID computing environments.

We evaluate the advantages obtained by blending supervised and unsupervised learning 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.


Text mining; Support Vector Machines

Related Project

CATCH - Inductive Inference for Large Scale Data Bases Text CATegorization


ICANNGA 2005, March 2005

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