1. CS 430 / INFO 430 Information Retrieval
Lecture 10
Evaluation of Retrieval Effectiveness 1

2. Course administration
Regrading requests
If you have a question about your grade, send a message to
the CMS system. The original grader and I will review your
query and I will reply to you if there is a grade change.
• If we clearly made a mistake, we will correct the grade.
• If we discover a mistake that was missed in the first grading, we will deduct points.
• If the program did not run for a simple reason (e.g., wrong files submitted), we will run it and regrade it, but remove points for poor submission.
• When the grade is a matter of judgment, no changes will be made.

3. Course administration
Assignment 2
• We suggest the JAMA package for singular value decomposition in Java. If you wish to use C++, you will need to find a similar package. We may suggest possible packages, but with no guarantees.
• You will have to select a suitable value of k, the number of dimensions. In your report explain how you selected k. With only 20 short files, the appropriate value of k for this corpus is almost certainly much less than the value of 100 used in the reading.

4. Course administration
Discussion Class 4
(a) Check the Web site for which sections to concentrate on.
(b) The PDF version of the file on the TREC site is damaged. Use the PostScript version, or the PDF version on the course Web site.

5. Evaluation
To place information retrieval on a systematic basis, we need repeatable criteria to evaluate how effective a system is in meeting the information needs of the user of the system.
This proves to be very difficult with a human in the loop. It proves hard to define:
• the task that the human is attempting
• the criteria to measure success

6. Studies of Retrieval Effectiveness
• The Cranfield Experiments, Cyril W. Cleverdon, Cranfield College of Aeronautics,
• SMART System, Gerald Salton, Cornell University,
• TREC, Donna Harman and Ellen Voorhees, National Institute of Standards and Technology (NIST),

7. Matching: Recall and Precision
With matching methods, if information retrieval were perfect ...
Every hit would be relevant to the original query, and every relevant item in the body of information would be found.
Precision: fraction (or percentage) of the hits that are
relevant, i.e., the extent to which the set of hits
retrieved by a query satisfies the requirement that
generated the query.
Recall: fraction (or percentage) of the relevant items that are
found by the query, i.e., the extent to which the query
found all the items that satisfy the requirement.

8. Recall and Precision with Exact Matching
Example
• Corpus of 10,000 documents, 50 on a specific topic
• Ideal search finds these 50 documents and reject all others
• Actual search identifies 25 documents; 20 are relevant but 5 were on other topics
• Precision: 20/25 = (80% of hits were relevant)
• Recall: 20/50 = 0.4 (40% of relevant were found)

9. Measuring Precision and Recall
Precision is easy to measure:
A knowledgeable person looks at each document that is identified and decides whether it is relevant.
In the example, only the 25 documents that are found need to be examined.
Recall is difficult to measure:
To know all relevant items, a knowledgeable person must go through the entire collection, looking at every object to decide if it fits the criteria.
In the example, all 10,000 documents must be examined.

10. Ranking Methods: Precision and Recall
Precision and recall are defined for a fixed set of hits, e.g., Boolean retrieval.
Their use needs to be modified for a ranked list of results.

11. Ranking Methods
Precision and recall measure the results of a single query using a specific search system applied to a specific set of documents.
Matching methods:
Precision and recall are single numbers.
Ranking methods:
Precision and recall are functions of the rank order.

12. Evaluating Ranking: Recall and Precision
If information retrieval were perfect ...
Every document relevant to the original information need would be ranked above every other document.
With ranking, precision and recall are functions of the rank order.
Precision(n): fraction (or percentage) of the n most highly
ranked documents that are relevant.
Recall(n): fraction (or percentage) of the relevant items that
are in the n most highly ranked documents.

13. Precision and Recall with Ranking
Example
"Your query found 349,871 possibly relevant documents. Here are the first eight."
Examination of the first 8 finds that 5 of them are relevant.

14. Graph of Precision with Ranking: P(r)
Relevant? Y N Y Y N Y N Y
Precision P(r) 1
1/ / / / / / / /8
Rank r

15. Ranked retrieval: Recall and precision after retrieval of n documents
n relevant recall precision
1 yes
2 yes
3 no
4 yes
5 no
6 yes
7 no
8 no
9 no
10 no
11 no
12 no
13 yes
14 no
SMART system using Cranfield data, 200 documents in aeronautics of which 5 are relevant

16. Precision-recall graph
Note: Some authors plot recall against precision. 1 2
1.0 4
0.75 6 3 5
0.5 13 12
0.25
200
recall
0.25
0.5
0.75
1.0

17. 11 Point Precision (Recall Cut Off)
p(n) is precision at that point where recall has first reached n
Define 11 standard recall points p(r0), p(r1), ... p(r10),
where p(rj) = p(j/10)
Note: if p(rj) is not an exact data point, use interpolation

18. Example: SMART System on Cranfield Data
Recall Precision
Precision values in blue are actual data.
Precision values in red are by interpolation (by convention equal to the next actual data value).

19. Recall cutoff graph: choice of interpolation points
precision 1 2
The blue line is the recall cutoff graph.
1.0 4
0.75 6 3 5
0.5 13 12
0.25
200
recall
0.25
0.5
0.75
1.0

20. Average precision
Average precision for a single topic is the mean of the precision obtained after each relevant document is obtained.
Example:
p = ( ) / 5
= 0.75
Mean average precision for a run consisting of many topics is the mean of the average precision scores for each individual topic in the run.
Definitions from TREC-8

21. Relevance Recall and precision: depend on concept of relevance
Relevance is a context-, task-dependent property of documents
"Relevance is the correspondence in context between an information requirement statement ... and an article (a document), that is, the extent to which the article covers the material that is appropriate to the requirement statement."
F. W. Lancaster, 1979

22. Relevance How stable are relevance judgments?
• For textual documents, knowledgeable users have good agreement in deciding whether a document is relevant to an information requirement.
• There is less consistency with non-textual documents, e.g., a photograph.
• Attempts to have users give a level of relevance, e.g., on a five point scale, are inconsistent.

23. Relevance judgments (TREC)
• In the TREC-1 experiments, each topic was judged by a single assessor, who also set the topic statement.
• In TREC-2, a sample of the topics and documents was rejudged by second expert assessor. The average agreement was about 80%.
• In TREC-4, all topics were rejudged by two additional assessors, with 72% agreement among all three assessors.

24. Relevance judgments (TREC)
However:
• In the TREC-4 tests, most of the agreement was among the documents that all assessors agreed were non-relevant
• 30% of documents judged relevant by the first assessor, were judged non-relevant by both additional assessors.
Using data from TREC-4 and TREC-6, Voorhees estimates a
practical upper bound of 65% precision at 65% recall, as the
level at which human experts agree with one another.

25. Relevance as a Set Comparison
D = set of documents
A = set of documents that satisfy some user-based criterion
B = set of documents identified by the search system

26. Measures based on relevance
retrieved relevant | A  B |
relevant | A |
retrieved | B |
retrieved not-relevant | B - A  B |
not-relevant | D - A |
recall = =
precision = =
fallout = =

27. Combining Recall and Precision: Normalized Symmetric Difference
Symmetric Difference: The set of elements belonging to one but not both of two given sets.
D = set of documents B A
Retrieved
Relevant
Symmetric difference, S = A  B - A  B
Normalized symmetric difference = |S| / 2 (|A| + |B|) =
{ } 1 2 1
(1/recall + 1/precision)

28. Normalized recall measure
actual ranks
worst ranks
ideal ranks
recall 5 10 15
195
200
ranks of retrieved documents

29. Normalized recall area between actual and worst
area between best and worst
Normalized recall =
i = 1 n 
i = 1 n 
ri i
n(N - n)
Rnorm = (after some mathematical
manipulation)

30. Cranfield Collection The first Information Retrieval test collection
1,400 documents on aerodynamics
Queries:
225 queries, with a list of the documents that should be retrieved for each query

31. Cranfield Second Experiment
Comparative efficiency of indexing systems:
(Universal Decimal Classification, alphabetical subject index, a special facet classification, Uniterm system of co-ordinate indexing)
Four indexes prepared manually for each document in three batches of 6,000 documents -- total 18,000 documents, each indexed four times. The documents were reports and paper in aeronautics.
Indexes for testing were prepared on index cards and other cards.
Very careful control of indexing procedures.

32. Cranfield Second Experiment (continued)
Searching:
• 1,200 test questions, each satisfied by at least one document
• Reviewed by expert panel
• Searches carried out by 3 expert librarians
• Two rounds of searching to develop testing methodology
• Subsidiary experiments at English Electric Whetstone
Laboratory and Western Reserve University

33. The Cranfield Data
The Cranfield data was made widely available and used by other
researchers
• Salton used the Cranfield data with the SMART system (a) to study the relationship between recall and precision, and (b) to compare automatic indexing with human indexing
• Sparc Jones and van Rijsbergen used the Cranfield data for experiments in relevance weighting, clustering, definition of test corpora, etc.

34. Cranfield Experiments -- Measures of Effectiveness for Matching Methods
Cleverdon's work was applied to matching methods. He made extensive use of recall and precision, based on concept of relevance.
precision (%)
Each x represents one search. The graph illustrates the trade-off between precision and recall. x x x x x x x x x x
recall (%)

35. Typical precision-recall graph for different queries
Using Boolean type queries
Narrow, specific query
1.0
0.75
Note: Some authors plot recall against precision.
0.5
Broad, general query
0.25
recall
0.25
0.5
0.75
1.0

36. A Crucial Cranfield Results
• The various manual indexing systems have similar retrieval efficiency
• Retrieval effectiveness using automatic indexing can be at
least as effective as manual indexing with controlled
vocabularies
-> original results from the Cranfield + SMART experiments
(published in 1967)
-> considered counter-intuitive
-> other results since then have supported this conclusion