We present a new semi-supervised training procedure for conditional random fields (CRFs) that can be used to train sequence segmentors and labelers from a combination of labeled a...
Feng Jiao, Shaojun Wang, Chi-Hoon Lee, Russell Gre...
Using unlabeled data to help supervised learning has become an increasingly attractive methodology and proven to be effective in many applications. This paper applies semi-supervi...
Current semi-supervised incremental learning approaches select unlabeled examples with predicted high confidence for model re-training. We show that for many applications this dat...
Database selection is an important step when searching over large numbers of distributed text databases. The database selection task relies on statistical summaries of the databas...
In recent years, active learning methods based on experimental design achieve state-of-the-art performance in text classification applications. Although these methods can exploit ...