12. A computer-implemented method of analyzing effectiveness of a text correction tool, comprising: testing an error correction model of the text correction tool using a training corpus of original text and annotated text; receiving flagged text from the text correction tool based on the testing; applying simulated user actions to the flagged text based on a user model; employing an adaptive learning algorithm that learns based on implicit accepts and implicit ignores relative to the flagged text; generating and sending updates to the error correction model based on the simulated user actions; and computing quality data that represents effectiveness of the adaptive learning algorithm in learning the error correction model.
12. A computer-implemented method of analyzing effectiveness of a text correction tool, comprising: testing an error correction model of the text correction tool using a training corpus of original text and annotated text; receiving flagged text from the text correction tool based on the testing; applying simulated user actions to the flagged text based on a user model; employing an adaptive learning algorithm that learns based on implicit accepts and implicit ignores relative to the flagged text; generating and sending updates to the error correction model based on the simulated user actions; and computing quality data that represents effectiveness of the adaptive learning algorithm in learning the error correction model. 15. The method of claim 12 , further comprising tuning the adaptive learning algorithm based on at least one of biases related to acceptance/rejection of flagged text, orthographic errors made, or frequency of use of the flagged text in a search index.