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
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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