A Sequential Model for Multi-Class Classification

TL;DR

This paper introduces a sequential learning model designed for multi-class classification problems, especially effective in NLP domains, by iteratively narrowing down candidate classes to improve decision accuracy.

Contribution

The paper proposes a novel sequential approach for multi-class classification that maintains high probability of including the true class while reducing candidate sets, with theoretical and practical validation.

Findings

Model effectively narrows candidate classes in NLP tasks.

Theoretical properties support model's reliability.

Experiment demonstrates improved part-of-speech tagging accuracy.

Abstract

Many classification problems require decisions among a large number of competing classes. These tasks, however, are not handled well by general purpose learning methods and are usually addressed in an ad-hoc fashion. We suggest a general approach -- a sequential learning model that utilizes classifiers to sequentially restrict the number of competing classes while maintaining, with high probability, the presence of the true outcome in the candidates set. Some theoretical and computational properties of the model are discussed and we argue that these are important in NLP-like domains. The advantages of the model are illustrated in an experiment in part-of-speech tagging.