Committee-based active learning for dependency parsing

Abstract

Annotations on structured corpora provide a foundational instrument for emerging linguistic research. To generate annotations automatically, data-driven dependency parsers need a large annotated corpus to learn from. But these annotations are expensive to collect and require a labor intensive task. In order to reduce the costs of annotation, we provide a novel framework in which a committee of dependency parsers collaborate to improve their efficiency using active learning. © 2013 Springer-Verlag.