Transcript No Slide Title

IR System Evaluation
Farhad Oroumchian
IR System Evaluation
• System-centered strategy
– Given documents, queries, and relevance judgments
– Try several variations on the retrieval system
– Measure which gets more good docs near the top
• User-centered strategy
– Given several users, and at least 2 retrieval systems
– Have each user try the same task on both systems
– Measure which system works the “best”
Which is the Best Rank Order?
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g
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h
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Measures of Effectiveness
• Good measures of effectiveness should
– Capture some aspect of what the user wants
– Have predictive value for other situations
• Different queries, different document collection
– Be easily replicated by other researchers
– Be expressed as a single number
• Allows two systems to be easily compared
• No measures of effectiveness are that good!
Some Assumptions
• Unchanging, known queries
– The same queries are used by each system
• Binary relevance
– Every document is either relevant or it is not
• Unchanging, known relevance
– The relevance of each doc to each query is known
• But only used for evaluation, not retrieval!
• Focus on effectiveness, not efficiency
Exact Match MOE
• Precision
– How much of what was found is relevant?
• Often of interest, particularly for interactive searching
• Recall
– How much of what is relevant was found?
• Particularly important for law and patent searches
• Fallout
– How much of what was irrelevant was rejected?
• Useful when different size collections are compared
The Contingency Table
Action
Doc
Relevant
Retrieved
Not Retrieved
Relevant Retrieved
Relevant Rejected
Not relevant Irrelevant Retrieved Irrelevant Rejected
Relevant Retrieved
Precision 
Retrieved
Relevant Retrieved
Recall 
Relevant
Irrelevant Rejected
Fallout 
Not Relevant
MOE for Ranked Retrieval
• Start with the first relevant doc on the list
– Compute recall and precision at that point
• Move down the list, 1 relevant doc at a time
– Computing recall and precision at each point
• Plot precision for every value of recall
– Interpolate with a nonincreasing step function
• Repeat for several queries
• Average the plots at every point
R
R
The Precision-Recall Curve
Action
Doc=10
R
Relevant=4
Not relevant=6
R
1
0.9
0.8
0.7
0.6
0.5
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0.1
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Precision
Recall
Retrieved
Not Retrieved
Relevant Retrieved
Relevant Rejected
Irrelevant Retrieved Irrelevant Rejected
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0.8
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0.6
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0.4
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Precision
0.2
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InterpolatedPrecision
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1 2 3 4 5 6 7 8 9 10
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Recall
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Recall
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Single-Number MOE
• Precision at a fixed number of documents
– Precision at 10 docs is the “AltaVista measure”
• Precision at a given level of recall
– Adjusts for the total number of relevant docs
• Average precision
– Average of precision at recall=0.0, 0.1, …, 1.0
– Area under the precision/recall curve
• Breakeven point
– Point where precision = recall
Single-Number MOE
Precision at recall=0.1
Average Precision
1
Breakeven Point
0.9
0.8
0.7
Precision at 10 docs
0.6
0.5
0.4
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0.1
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Recall
0.8
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Single-Number MOE Weaknesses
• Precision at 10 documents
– Pays no attention to recall
• Precision at constant recall
– A specific recall fraction is rarely the user’s goal
• Breakeven point
– Nobody ever searches at the breakeven point
• Average precision
– Users typically operate near an extreme of the curve
• So the average is not very informative
Why Choose Average Precision?
• It is easy to trade between recall and precision
– Adding related query terms improves recall
• But naive query expansion techniques kill precision
– Limiting matches by part-of-speech helps precision
• But it almost always hurts recall
• Comparisons should give some weight to both
– Average precision is a principled way to do this
• Rewards improvements in either factor
How Much is Enough?
• The maximum average precision is 1.0
– But inter-rater reliability is 0.8 or less
– So 0.8 is a practical upper bound at every point
• Precision 0.8 is sometimes seen at low recall
• Two goals
– Achieve a meaningful amount of improvement
• This is a judgment call, and depends on the application
– Achieve that improvement reliably across queries
• This can be verified using statistical tests
Statistical Significance Tests
• How sure can you be that an observed
difference doesn’t simply result from the
particular queries you chose?
Experiment 1
Query System A System B
Experiment 1
Query System A System B
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Average
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Average
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0.02
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0.16
0.58
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0.12
0.20
0.76
0.07
0.37
0.21
0.02
0.91
0.46
0.40
The Sign Test
• Compare the average precision for each query
– Note which system produces the bigger value
• Assume that either system is equally likely to
produce the bigger value for any query
• Compute the probability of the outcome you got
– Any statistics package contains the formula for this
• Probabilities<0.05 are “statistically significant”
– But they still need to pass the “meaningful” test!
The Students T-Test
• More powerful than the sign test
– If the assumptions are satisfied
• Compute the average precision difference
– On a query by query basis for enough queries to
approximate a normal distribution
• Assume that the queries are independent
• Compute the probability of the outcome you got
– Again, any statistics package can be used
• A probability>0.05 is “statistically significant”
Obtaining Relevance Judgments
• Exhaustive assessment can be too expensive
– TREC has 50 queries for >1 million docs each year
• Random sampling won’t work either
– If relevant docs are rare, none may be found!
• IR systems can help focus the sample
– Each system finds some relevant documents
– Different systems find different relevant documents
– Together, enough systems will find most of them
Pooled Assessment Methodology
• Each system submits top 1000 documents
• Top 100 documents for each are judged
– All are placed in a single pool
– Duplicates are eliminated
– Placed in an arbitrary order to avoid bias
• Evaluated by the person that wrote the query
• Assume unevaluated documents not relevant
– Overlap evaluation shows diminishing returns
• Compute average precision over all 1000 docs
TREC Overview
• Documents are typically distributed in April
• Topics are distributed June 1
– Queries are formed from topics using standard rules
• Top 1000 selections are due August 15
• Special interest track results due 2 weeks later
– Cross-language IR, Spoken Document Retrieval, …
• Relevance judgments available in October
• Results presented in late November each year
Concerns About Precision/Recall
• Statistical significance may be meaningless
• Average precision won’t reveal curve shape
– Averaging over recall washes out information
• How can you know the quality of the pool?
• How to extrapolate to other collections?
‫‪Project Test Collection‬‬
‫• مواد قانون از سيستم قوانين دادهپردازي‬
‫• ‪ 41 query‬از سيستم قوانين‬
‫‪• Relevance judgments keyed to ItemID‬‬
‫‪– Relevance is in scale 0-4‬‬
‫نامربوط بطور كلي ‪0=-‬‬
‫نامربوط =‪1‬‬
‫كمي مربوط =‪2‬‬
‫مربوط =‪3‬‬
‫كامال مربوط =‪4‬‬
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‫تلقي ميشوند نامربوط معموال ‪3‬و ‪ 4‬مربوط و ‪– 0،1،2‬‬
Project Overview
• Install or write the software the software
• Choose the parameters
– Stopwords, stemming, term weights, etc.
• Index the document collection
– This may require some format-specific tweaking
• Run the 20 queries
• Compute average precision and other measures
• Test query length effect for statistical significance
Team Project User Studies
• Measure value of some part of the interface
– e.g., selection interface with and without titles
• Choose a dependent variable to measure
– e.g., number of documents examined
• Run a pilot study with users from your team
– Fine tune your experimental procedure
• Run the experiment with at least 3 subjects
– From outside your team (may be in the class)