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Fine-Grained Error Analysis and Fair Evaluation of Labeled Spans

The traditional evaluation of labeled spans with precision, recall, and F1-score has undesirable effects due to double penalties. Annotations with incorrect label or boundaries count as two errors instead of one, despite being closer to the target annotation than false positives or false negatives. In this paper, new error types are introduced, which more accurately reflect true annotation quality and ensure that every annotation counts only once. An algorithm for error identification in flat and multi-level annotations is presented and complemented with a proposal on how to calculate meaningful precision, recall, and F1-scores based on the more fine-grained error types. The exemplary application to three different annotation tasks (NER, chunking, parsing) shows that the suggested procedure not only prevents double penalties but also allows for a more detailed error analysis, thereby providing more insight into the actual weaknesses of a system.