A Dynamic Centroid Text Classification Approach by Learning from Unlabeled Data

DOI

10.2991/icmt-13.2013.174How to use a DOI?

Keywords

Text classification Dynamic centroid Confidence Unlabeled texts

Abstract

The centroid-based classification has proved to be a simple and yet efficient method for text classification. However, the performance of centroid-based classifier depends heavily on the quantity of labeled training set. It is easy and cheap to collect enormous unlabeled data from digital resources, while it is difficult and costly to label these data for training classifiers. To address this problem, we propose a dynamic centroid text classification approach which learns from unlabeled texts to construct dynamic centroids. The main idea of the approach is to take the unlabeled texts with high classifying confidence into consideration to adjust the centroids dynamically. Experiments on two public corpora have indicated the effectiveness of our text classification approach in the case of spare labeled training set.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jiang Cuicui
AU  - Zhu Dingju
AU  - Jiang Qingshan
PY  - 2013/11
DA  - 2013/11
TI  - A Dynamic Centroid Text Classification Approach by Learning from Unlabeled Data
BT  - Proceedings of 3rd International Conference on Multimedia Technology(ICMT-13)
PB  - Atlantis Press
SP  - 1413
EP  - 1422
SN  - 1951-6851
UR  - https://doi.org/10.2991/icmt-13.2013.174
DO  - 10.2991/icmt-13.2013.174
ID  - Cuicui2013/11
ER  -