Maximal Margin Labeling for Multi-Topic Text Categorization

Published 1 December 2004

Hideto Kazawa, Tomonori Izumitani, Hirotoshi Taira, Eisaku Maeda

Citations113

TL;DR

The proposed algorithm, Maximal Margin Labeling (MML), treats all possible labels as independent classes and learns a multi- class classifier on the induced multi-class categorization problem.

Abstract

In this paper, we address the problem of statistical learning for multitopic text categorization (MTC), whose goal is to choose all relevant topics (a label) from a given set of topics. The proposed algorithm, Maximal Margin Labeling (MML), treats all possible labels as independent classes and learns a multi-class classifier on the induced multi-class categorization problem. To cope with the data sparseness caused by the huge number of possible labels, MML combines some prior knowledge about label prototypes and a maximal margin criterion in a novel way. Experiments with multi-topic Web pages show that MML outperforms existing learning algorithms including Support Vector Machines. 1 Multi-topic Text Categorization (MTC) This paper addresses the problem of learning for multi-topic text categorization (MTC), whose goal is to select all topics relevant to a text from a given set of topics. In MTC, multiple topics may be relevant to a single text. We thus call a set of topics label, and say

Keywords

Computer Science

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