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Natural Language Processing with Java and LingPipe Cookbook by Krishna Dayanidhi, Breck Baldwin

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Evaluation of classifiers – the confusion matrix

Evaluation is incredibly important in building solid NLP systems. It allows developers and management to map a business need to system performance, which, in turn, helps communicate system improvement to vested parties. "Well, uh, the system seems to be doing better" does not hold the gravitas of "Recall has improved 20 percent, and the specificity is holding well with 50 percent more training data".

This recipe provides the steps for the creation of truth or gold standard data and tells us how to use this data to evaluate the performance of our precompiled classifier. It is as simple as it is powerful.

Getting ready

You might have noticed the headers from the output of the CSV writer and the suspiciously ...

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