I256 applied natural language processing
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I256: Applied Natural Language Processing. Marti Hearst Sept 27, 2006. Evaluation Measures. Evaluation Measures. Precision: Proportion of those you labeled X that the gold standard thinks really is X #correctly labeled by alg/ all labels assigned by alg

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I256: Applied Natural Language Processing

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I256: Applied Natural Language Processing

Marti Hearst

Sept 27, 2006


Evaluation Measures


Evaluation Measures

  • Precision:

    • Proportion of those you labeled X that the gold standard thinks really is X

    • #correctly labeled by alg/ all labels assigned by alg

    • #True Positive / (#True Positive + #False Positive)

  • Recall:

    • Proportion of those items that are labeled X in the gold standard that you actually label X

    • #correctly labeled by alg / all possible correct labels

    • #True Positive / (#True Positive + # False Negative)


F-measure

  • Can “cheat” with precision scores by labeling (almost) nothing with X.

  • Can “cheat” on recall by labeling everything with X.

  • The better you do on precision, the worse on recall, and vice versa

  • The F-measure is a balance between the two.

    • 2*precision*recall / (recall+precision)


Evaluation Measures

  • Accuracy:

    • Proportion that you got right

    • (#True Positive + #True Negative) / N

      N = TP + TN + FP + FN

  • Error:

    • (#False Positive + #False Negative)/N


Prec/Recall vs. Accuracy/Error

  • When to use Precision/Recall?

    • Useful when there are only a few positives and many many negatives

    • Also good for ranked ordering

      • Search results ranking

  • When to use Accuracy/Error

    • When every item has to be judged, and it’s important that every item be correct.

    • Error is better when the differences between algorithms are very small; let’s you focus on small improvements.

      • Speech recognition


Evaluating Partial Parsing

  • How do we evaluate it?


Evaluating Partial Parsing


Testing our Simple Fule

  • Let’s see where we missed:


Update rules; Evaluate Again


Evaluate on More Examples


Incorrect vs. Missed

  • Add code to print out which were incorrect


Missed vs. Incorrect


What is a good Chunking Baseline?


The Tree Data Structure


Baseline Code (continued)


Evaluating the Baseline


Cascaded Chunking


Next Time

  • Summarization


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