Semi-automated named entity annotation
Kuzman Ganchev, Fernando M. B. Pereira, Mark A. Mandel, Steven Carroll, Peter S. White · 2007
We investigate a way to partially automate corpus annotation for named entity recognition, by requiring only binary decisions from an annotator. Our approach is based on a linear sequence model trained using a k-best MIRA learning algorithm. We ask an annotator to decide whether each mention produced by a high recall tagger is a true mention or a false positive. We conclude that our approach can reduce the effort of extending a seed training corpus by up to 58%.