1

I256: Applied Natural Language Processing

Marti HearstSept 27, 2006 

 

2

Evaluation Measures

3

Evaluation MeasuresPrecision:

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)

4

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 versaThe F-measure is a balance between the two.

2*precision*recall / (recall+precision)

5

Evaluation Measures

Accuracy:Proportion that you got right (#True Positive + #True Negative) / N

N = TP + TN + FP + FNError:

(#False Positive + #False Negative)/N

6

Prec/Recall vs. Accuracy/ErrorWhen to use Precision/Recall?

Useful when there are only a few positives and many many negativesAlso good for ranked ordering

– Search results rankingWhen 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

7

Evaluating Partial Parsing

How do we evaluate it?

8

Evaluating Partial Parsing

9

Testing our Simple FuleLet’s see where we missed:

10

Update rules; Evaluate Again

11

Evaluate on More Examples

12

Incorrect vs. MissedAdd code to print out which were incorrect

13

Missed vs. Incorrect

14

What is a good Chunking Baseline?

15

16

The Tree Data Structure

17

Baseline Code (continued)

18

Evaluating the Baseline

19

Cascaded Chunking

20

21

Next Time

Summarization