The focus of this paper is to address the knowledge acquisition bottleneck for Named Entity Recognition (NER) of mutations, by analysing different approaches to build manually-annotated data. We address first the impact of using a single annotator vs two annotators, in order to measure whether multiple annotators are required. Once we evaluate the performance loss when using a single annotator, we apply different methods to sample the training data for second annotation, aiming at improving the quality of the dataset without requiring a full pass. We use held-out double-annotated data to build two scenarios with different types of rankings: similarity-based and confidence based. We evaluate both approaches on: (i) their ability to identify training instances that are erroneous (cases where single-annotator labels differ from double-annotation after discussion), and (ii) on Mutation NER performance for state-of-the-art classifiers after integrating the fixes at different thresholds.
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