Annotating named entities in clinical text by combining pre-annotation and active learning
Maria Skeppstedt · Meeting of the Association for Computational Linguistics · 2013
For expanding a corpus of clinical text, annotated for named entities, a method that combines pre-tagging with a version of active learning is proposed. In order to facilitate annotation and to avoid bias, two alternative automatic pre-taggings are presented to the annotator, without revealing which of them is given a higher confidence by the pre-tagging system. The task of the annotator is to select the correct version among these two alternatives. To minimise the instances in which none of the presented pre-taggings is correct, the texts presented to the annotator are actively selected from a pool of unlabelled text, with the selection criterion that one of the presented pre-taggings should have a high probability of being correct, while still being useful for improving the result of an automatic classifier.