In active dual supervision, not only informative examples but also features are selected for labeling to build a high quality classifier with low cost. However, how to measure the informativeness for both examples and feature on the same scale has not been well solved. In this paper, we propose a non-negative matrix factorization based approach to address this issue. We first extend the matrix factorization framework to explicitly model the corresponding relationships between feature classes and examples classes. Then by making use of the reconstruction error, we propose a unified scheme to determine which feature or example a classifier is most likely to benefit from having labeled. Empirical results demonstrate the effectiveness of our proposed methods.
REF further extended the matrix factorization framework to model dual supervision from both document and word labels.
A Non-negative Matrix Factorization Based Approach for Active Dual Supervision from Document and Word Labels
EMNLP
Computer Science