Paper: A Non-negative Matrix Factorization Based Approach for Active Dual Supervision from Document and Word Labels

ACL ID D11-1088
Title A Non-negative Matrix Factorization Based Approach for Active Dual Supervision from Document and Word Labels
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2011
Authors

In active dual supervision, not only informa- tive examples but also features are selected for labeling to build a high quality classifier with low cost. However, how to measure the infor- mativeness for both examples and feature on the same scale has not been well solved. In this paper, we propose a non-negative matrix factorizationbasedapproachtoaddressthisis- sue. We first extend the matrix factorization frameworktoexplicitlymodelthecorrespond- ing 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 exam- ple a classifier is most likely to benefit from having labeled. Empirical results demonstrate the effectiveness of our proposed methods.