A Dynamic Centroid Text Classification Approach by Learning from Unlabeled Data
Jiang Cuicui, Zhu Dingju, Jiang Qingshan
Available Online November 2013.
- https://doi.org/10.2991/icmt-13.2013.174How to use a DOI?
- Text classification Dynamic centroid Confidence Unlabeled texts
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 PB - Atlantis Press SP - 1413 EP - 1422 SN - 1951-6851 UR - https://doi.org/10.2991/icmt-13.2013.174 DO - https://doi.org/10.2991/icmt-13.2013.174 ID - Cuicui2013/11 ER -