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JACIII Vol.18 No.5 pp. 818-822
doi: 10.20965/jaciii.2014.p0818
(2014)

Paper:

Label Propagation for Text Classification Using Latent Topics

Akiko Eriguchi and Ichiro Kobayashi

Advanced Sciences, Graduate School of Humanities and Sciences, Ochanomizu University, 2-1-1 Otsuka, Bunkyo-ku, Tokyo 112-8610, Japan

Received:
December 15, 2013
Accepted:
June 1, 2014
Published:
September 20, 2014
Keywords:
graph-based semi-supervised learning, label propagation, text classification, latent Dirichlet allocation
Abstract

The objective of this paper is to raise the accuracy of multiclass text classification through Graph-Based Semi-Supervised Learning (GBSSL). In GBSSL, it is essential to construct a proper graph which expresses the relation among nodes. We propose a method to construct a similarity graph by employing both surface information and latent information to express similarity between nodes. Experimenting on a Reuters-21578 corpus, we have confirmed that our proposal works well in raising the accuracy of GBSSL in a multiclass text classification task.

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Last updated on Nov. 20, 2017