GM-RKB WTEC System's False Negative (FN) Classification

A GM-RKB WTEC System's False Negative (FN) Classification is a False Negative Classification where a wikitext error is wrongly disregarded by the GM-RKB WTEC System.

• Example(s):
  • …
• Counter-Example(s):
  • GM-RKB WTEC System's False Positive (FP) Classification,
  • GM-RKB WTEC System's True Negative (TN) Classification,
  • GM-RKB WTEC System's True Positive (TP) Classification,
  • GM-RKB WTEC System's Detected Not-Fixed (DN) Classification.
• See: GM-RKB WikiText Error Correction (WTEC) Task, GM-RKB WTEC System's Performance Evaluation Metric, WikiText Error Correction (WTEC) System.

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References

2020

• (Melli et al., 2020) ⇒ Gabor Melli, Abdelrhman Eldallal, Bassim Lazem, and Olga Moreira. (2020). “GM-RKB WikiText Error Correction Task and Baselines.”. In: Proceedings of LREC 2020 (LREC-2020).
  • QUOTE: Each character in the WEC system's output text is grouped into one of the following five outcomes:
    • True Positive (TP): the source character was an error; the model correctly fixed it.
    • False Positive (FP): although the source character was not an error; however, the model tried to fix it introducing a new error instead.
    • True Negative (TN): the source character was not an error; the model correctly disregarded it as an error and did not try to fix it.
    • False Negative (FN): the source character was an error; however, the model failed to detect it and processed the error as the correct character.
    • Detected Not-Fixed (DN): the source character was an error; the model successfully detected the error but failed to correct it, or used the wrong character to fixed. (e.g. the correct repair was changing 'a' to 'b' but the model changed 'b' to 'c' instead of 'a').

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