Partially Supervised Classification of Text Documents

This paper shows that the problem of identifying documents from a set of documents of a particular topic or class P and a large set M of mixed documents, and that under appropriate conditions, solutions to the constrained optimization problem will give good solution to the partially supervised classification problem.

Abstract

We investigate the following problem: Given a set of documents of a particular topic or class È, and a large set Å of mixed documents that contains documents from class È and other types of documents, identify the documents from class È in Å. The key feature of this problem is that there is no labeled non-È document, which makes traditional machine learning techniques inapplicable, as they all need labeled documents of both classes. We call this problem partially supervised classification. In this paper, we show that this problem can be posed as a constrained optimization problem and that under appropriate conditions, solutions to the constrained optimization problem will give good solutions to the partially supervised classification problem. We present a novel technique to solve the problem and demonstrate the effectiveness of the technique through extensive experimentation. 1.