Effective top-down active learning for hierarchical text classification

4Citations
Citations of this article
7Readers
Mendeley users who have this article in their library.
Get full text

Abstract

Hierarchical text classification is an important task in many real-world applications. To build an accurate hierarchical classification system with many categories, usually a very large number of documents must be labeled and provided. This can be very costly. Active learning has been shown to effectively reduce the labeling effort in traditional (flat) text classification, but few works have been done in hierarchical text classification due to several challenges. A major challenge is to reduce the so-called out-of-domain queries. Previous state-of-the-art approaches tackle this challenge by simultaneously forming the unlabeled pools on all the categories regardless of the inherited hierarchical dependence of classifiers. In this paper, we propose a novel top-down hierarchical active learning framework, and effective strategies to tackle this and other challenges. With extensive experiments on eight real-world hierarchical text datasets, we demonstrate that our strategies are highly effective, and they outperform the state-of-the-art hierarchical active learning methods by reducing 20% to 40% queries. © Springer-Verlag 2013.

Cite

CITATION STYLE

APA

Li, X., Ling, C. X., & Wang, H. (2013). Effective top-down active learning for hierarchical text classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7819 LNAI, pp. 233–244). https://doi.org/10.1007/978-3-642-37456-2_20

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free