Maximum Margin Active Learning for Sequence Labeling with Different Length

Sequence labeling problem is commonly encountered in many natural language and query processing tasks. SVM is a supervised learning algorithm that provides a flexible and effective way to solve this problem. However, a large amount of training examples is often required to train SVM, which can be costly for many applications that generate long and complex sequence data. This paper proposes an active learning technique to select the most informative subset of unlabeled sequences for annotation by choosing sequences that have largest uncertainty in their prediction. A unique aspect of active learning for sequence labeling is that it should take into consideration the effort spent on labeling sequences, which depends on the sequence length. A new active learning technique is proposed to use dynamic programming to identify the best subset of sequences to be annotated, taking into account both the uncertainty and labeling effort. Experiment results show that our SVM active learning technique can significantly reduce the number of sequences to be labeled while outperforming other existing techniques.