Active learning for coreference resolution

Authors:
Timothy A. Miller;Dmitriy Dligach;Guergana K. Savova
Affiliations:
Children's Hospital Boston and Harvard Medical School, Boston, MA;Children's Hospital Boston and Harvard Medical School, Boston, MA;Children's Hospital Boston and Harvard Medical School, Boston, MA
Venue:
BioNLP '12 Proceedings of the 2012 Workshop on Biomedical Natural Language Processing
Year:
2012

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Abstract

Active learning can lower the cost of annotation for some natural language processing tasks by using a classifier to select informative instances to send to human annotators. It has worked well in cases where the training instances are selected one at a time and require minimal context for annotation. However, coreference annotations often require some context and the traditional active learning approach may not be feasible. In this work we explore various active learning methods for coreference resolution that fit more realistically into coreference annotation workflows.