c.watterscs64031 evaluation measures. c.watterscs64032 evaluation? effectiveness? for whom? for...

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Evaluation Measures

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Evaluation?

• Effectiveness?• For whom?• For what?• Efficiency?• Time?• Computational Cost?• Cost of missed information? Too much info?

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Studies of Retrieval Effectiveness

• The Cranfield Experiments, Cyril W. Cleverdon, Cranfield College of Aeronautics, 1957 –1968 (hundreds of docs)

• SMART System, Gerald Salton, Cornell University, 1964-1988 (thousands of docs,)

• TREC, Donna Harman, National Institute of Standards and Technology (NIST), 1992 - (millions of docs, 100k to 7.5M per set, training Q’s and test Q’s, 150 each)

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What can we measure?• ???• Algorithm (Efficiency)

– Speed of algorithm– Update potential of indexing scheme– Size of storage required– Potential for distribution & parallelism

• User Experience (Effectiveness)– How many of all relevant docs were found– How many were missed– How many errors in selection– How many need to be scanned before get good ones

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Measures based on relevance

RR

NN

NR RN

not retrieved not relevant

retrieved not relevant

retrieved relevant

not retrieved relevant

retrievednot re

trieved

not relevantrelevant

Doc set

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Relevance

• System always correct!!• Who judges relevance?• Inter-rater reliability• Early evaluations

– Done by panel of experts– 1-2000 abstracts of docs

• TREC experiments– Done automatically– thousands of docs– Pooling + people

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Defining the universe of relevant docs

• Manual inspection• Manual exhaustive search• Pooling (TREC)

– Relevant set is the union of multiple techniques

• Sampling– Take a random sample – Inspect– Estimate from the sample for the whole set

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Defining the relevant docs in a retrieved set (hit list)

• Panel of judges

• Individual users

• Automatic detection techniques– Vocabulary overlap with known relevant docs– metadata

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Estimates of RecallPooled system used by TREC depends on the quality of the set of nominated documents. Are there relevant documents not in the pool?

•

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Measures based on relevance

RR

NN

NR RN

not retrieved not relevant

retrieved not relevant

retrieved relevant

not retrieved relevant

retrievednot re

trieved

not relevantrelevant

Doc set

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Measures based on relevance RR RR + NR

RR RR + RN

RN RN + NN

recall =

precision =

fallout =

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Relevance Evaluation Measures

• Recall and Precision

• Single valued measures– Macro and micro averages– R-precision– E-measure– Swet’s measure

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Recall and Precision

• Recall– Proportion of relevant docs retrieved

• Precision– Proportion of retrieved docs that are relevant

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Formula (what do we have to work with?)

• Rq= number of docs in whole data set relevant to query, q

• Rr = number of docs in hit set (retrieved docs) that are relevant

• Rh= number of docs in hit set

• Recall = Precision =

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• Recall = Precision =

collection

RhRq

Rr

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• Recall = Precision =

• Rq={d3, d5, d9, d25, d39, d44, d56, d71, d89, d123}

• Rh={d6, d8, d9, d84, d123}

• Rr={ }

• Recall = Precision=

• ???what does that tell us??

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Recall Precision Graphs

precision

Recall

100%

100%

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Typical recall-precision graph

1.0

0.75

0.5

0.25

1.00.750.50.25

precision

recall

narrow, specific query

Broad, general query

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Recall-precision after retrieval of n documents

n relevant recall precision1 yes 0.2 1.02 yes 0.4 1.03 no 0.4 0.674 yes 0.6 0.755 no 0.6 0.606 yes 0.8 0.677 no 0.8 0.578 no 0.8 0.509 no 0.8 0.4410 no 0.8 0.4011 no 0.8 0.3612 no 0.8 0.3313 yes 1.0 0.3814 no 1.0 0.36

SMART system using Cranfield data, 200 documents in aeronautics of which 5 are relevant

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Recall-precision graph

1.0

0.75

0.5

0.25

1.00.750.50.25

recall

precision

1

23

45

612

13200

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Recall-precision graph

1.0

0.75

0.5

0.25

1.00.750.50.25

recall

precision

The red system appears better than the black, but is the difference statistically significant?

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Consider Rank

• Recall = Precision =

• Rq={d3, d5, d9, d25, d39, d44, d56, d71, d89, d123}

• Rh={d123, d84, d56, d6, d8 , d 9, d 511, d129 , d 187, d 25, d38 , d48 , d250 , d113 , d3}

• Rr={ }

• Recall = Precision=• What happens as we go through the hits?

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Standard Recall Levels

• Plot Precision for Recall =0%, 10%,….100%

P

R 100

100

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Consider two queries

• Rq={d3, d5, d9, d25, d39, d44, d56, d71, d89, d123}

• Rh={d123, d84, d56, d8 , d 9, d 511, d129 , d 25, d38, d3}

• Rq={d3, d5, d56, d89 , d90 , d 94, d129 , d206 , d500 d502}

• Rh={d12 ,d84, d56, d6, d8 , d 3, d 511, d129 ,d44 ,d89}

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Comparison of Query results

0

20

40

60

80

100

120

1 2 3 4 5 6 7 8

recall

precision

Q1

Q2

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P-R for Multiple Queries

• For each recall level average precision

• Avg Prec at recall r

• Nq is number of queries

• Pi(r) is prec at recall level r for ith query

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Comparison of two systems

0

20

40

60

80

100

120

1 2 3 4 5 6 7 8 9 10 11

recall

Avg

Ag2

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Macro and Micro Averaging

• Micro – average over each point

• Macro – average of averages per query

• example

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Statistical testsSuppose that a search is carried out on systems i and jSystem i is superior to system j if

recall(i) >= recall(j)precisions(i) >= precision(j)

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Statistical tests• The t-test is the standard statistical test for comparing two table of numbers, but depends on statistical assumptions of independence and normal distributions that do not apply to this data.

• The sign test makes no assumptions of normality and uses only the sign (not the magnitude) of the the differences in the sample values, but assumes independent samples.

• The Wilcoxon signed rank uses the ranks of the differences, not their magnitudes, and makes no assumption of normality but but assumes independent samples.

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II Single Value Measures

• E-Measure & F1 measure

• Swet’s Measure

• Expected Search Length

• etc

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E Measure & F1 Measure

• Weighted average of recall and precision

• Can increase importance of recall or precision

• F=1-E (bigger is better)

• Beta often =1

• Increase beta ???

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Normalized Recall

• recall is normalized against all relevant documents.

• Suppose there are N documents in

• the collection and out of which n are relevant. These n documents are ranked as i1, i2,…, in.

• normalized recall is calculated:

• Normalize recall = (ij - j) / (n * (N – n))

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Normalized recall measure

5 10 15 200195

ideal ranks

actual ranks

worst ranks

recall

ranks of retrieved documents

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Normalized recall area between actual and worst area between best and worstNormalized recall =

Rnorm = 1 - ri - i

n(N - n)

i = 1

n

i = 1

n

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Example: N=200 n=5 at 1,3,5,10,14

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Expected Search Length

• Number of non-relevant docs before relevant doc(s) are found

• Assume weak or no ordering

• Lq= j + i . s/(r+1)

• s is required # of relevant docs

• j is # non-relevant docs before get required #

• ??

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Problems with testing

• Determining relevant docs

• Setting up test questions

• Comparing results

• Understanding relevance of the results