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FairEval

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illorca commited on Nov 29, 2022

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 The traditional evaluation of NLP labeled spans with precision, recall, and F1-score leads to double penalties for
 close-to-correct annotations. As [Manning (2006)](https://nlpers.blogspot.com/2006/08/doing-named-entity-recognition-dont.html)
 argues in an article about named entity recognition, this can lead to undesirable effects when systems are optimized for these traditional metrics.
-Building on his ideas, [Katrin Ortmann (2022)](https://aclanthology.org/2022.lrec-1.150.pdf) develops FairEval.
 ## How to Use
 FairEval outputs the error count (TP, FP, etc.) and resulting scores (Precision, Recall and F1) from a reference list of

 The traditional evaluation of NLP labeled spans with precision, recall, and F1-score leads to double penalties for
 close-to-correct annotations. As [Manning (2006)](https://nlpers.blogspot.com/2006/08/doing-named-entity-recognition-dont.html)
 argues in an article about named entity recognition, this can lead to undesirable effects when systems are optimized for these traditional metrics.
+To address these issues, this metric provides an implementation of FairEval, proposed by [Ortmann (2022)](https://aclanthology.org/2022.lrec-1.150.pdf).
 ## How to Use
 FairEval outputs the error count (TP, FP, etc.) and resulting scores (Precision, Recall and F1) from a reference list of