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Google post gives a interesting explanation about True Positive, True Negative, False Positive, False Negative

True Positive (TP): Reality: A wolf threatened. Shepherd said: "Wolf." Outcome: Shepherd is a hero.

True Negative (TN): Reality: No wolf threatened. Shepherd said: "No wolf." Outcome: Everyone is fine.

False Positive (FP): Reality: No wolf threatened. Shepherd said: "Wolf." Outcome: Villagers are angry at shepherd for waking them up.

False Negative (FN): Reality: A wolf threatened. Shepherd said: "No wolf." Outcome: The wolf ate all the sheep.

In the context of CV, the classifier predicts if an image contains cat

True Positive (TP): 
Reality: an image contains cat. 
classifier predicts: cat.

True Negative (TN): 
Reality: an image does not contains cat. 
classifier predicts: no cat.

False Positive (FP): 
Reality: an image does not contains cat. 
classifier predicts: cat.

False Negative (FN): 
Reality: an image contains cat. 
classifier predicts: no cat.

Can anyone gives a concrete example of TP、TN、FP、FN like above, in the context of natural language processing?

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Imagine a hot news classifier.

True Positive (TP): Reality: a piece of hot news. classifier predicts: hot.

True Negative (TN): Reality: not a piece of hot news. classifier predicts: not hot.

False Positive (FP): Reality: not a piece of hot news. classifier predicts: hot.

False Negative (FN): Reality: a piece of hot news. classifier predicts: not hot.

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