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1 Retrieval Performance Evaluation Modern Information Retrieval by R. Baeza-Yates and B. Ribeiro-Neto Addison-Wesley, 1999. (Chapter 3)

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Presentation on theme: "1 Retrieval Performance Evaluation Modern Information Retrieval by R. Baeza-Yates and B. Ribeiro-Neto Addison-Wesley, 1999. (Chapter 3)"— Presentation transcript:

1 1 Retrieval Performance Evaluation Modern Information Retrieval by R. Baeza-Yates and B. Ribeiro-Neto Addison-Wesley, (Chapter 3)

2 2 Recall and Precision l Recall l Precision l Goal high recall and high precision

3 3 Recall and Precision

4 4 Precision Vs. Recall Figure l Rq={d 3,d 5,d 9,d 25,d 39,d 44,d 56,d 71,d 89,d 123 } l Aq={d 123,d 84,d 56,d 6,d 8,d 9,d 511,d 129,d 187,d 25,d 38,d 48,d 250, d 113,d 3 } »R=10%, P=100% »R=20%, P=66% »R=50%, P=33.3% »R>50%, P=0% l Precision at 11 standard recall levels »0%, 10%, 20%, …, 100%

5 5 Average Precision Values l To evaluate the retrieval performance of an algorithm over all test queries, we average the precision at each recall level » average precision at the recall level r »N q is the number of queries used »P i (r) is the precision at recall level r for query i

6 6 Precision Interpolation l Rq={d 3,d 56,d 129 } l Aq={d 123,d 84,d 56,d 6,d 8,d 9,d 511,d 129,d 187,d 25,d 38,d 48,d 250, d 113,d 3 } »R=33%, P=33% »R=66%, P=25% »R=100%, P=20% l Let r j, j in {0, 1, 2, …, 10}, be a reference to the standard j-th recall level.

7 7 Additional Approach l Average precision at document cutoff points »For instance, we can compute the average precision when 5, 10, 15, 20, 30, 50, 100 relevant documents have been seen.

8 8 Single Value Summaries l Average Precision at Seen Relevant Documents »The idea is to generate a single value summary of the ranking by averaging the precision figures obtained after each new relevant document is observed »e.g. for example 1: ( )/5 »This measure favors systems which retrieve relevant documents quickly

9 9 Single Value Summaries (Cont.) l R-Precision »The idea here is to generate a single value summary of the ranking by computing the precision at the R-th position in the ranking, where R is the total number of relevant documents »e.g. for example 1: R-Precision is 0.4 »e.g. for example 2: R-Precision is 0.3 »The R-precision measure is useful for observing the behavior of an algorithms for each individual

10 10 Single Value Summaries (Cont.) l Precision Histograms »Use R-precision measures to compare the retrieval history of two algorithms through visual inspection »RP A/B (i)=RP A (i)-RP B (i)

11 11 Reference Collections l Small Collection »The ADI Collection (documents on information science) »INSPEC (abstracts on electronics, computer, and physics) »Medlars (medial article) »The CACM Collection »The ISI Collection l Large Collection »The TREC Collection

12 12 The TREC Collection l Initiated by Donna Harman at NIST (National Institute of Standards and Technology) in 1990s l Co-sponsored by the Information Technology Office of the DARPA as part of the TIPSTER Text Program

13 13 The Documents Collection at TREC l Resource »WSJ: Wall Stree Journal »AP: Associated Press (news wire) »ZIFF: Computer Selects (articles), Ziff-Davis »FR: Federal Register »DOE, SJMN, PAT, FT, CR, FBIS, LAT l Size »TREC-3: 2GB »TREC-6: 5.8GB »US$200 in 1998

14 14 TREC document example

15 15 The Example Information Requests (Topics) l 350 topics for the first six TREC Conference l Topic: »1-150: TREC-1 and TREC-2 –long-standing information needs » : TREC-3 –simpler structure » : TREC-4 –even shorter » : TREC-5 » : TREC-6

16 16 TREC Topic Example

17 17 The Relevant Documents for Each Topic l Pooling Method »The set of relevant documents for each example information request (topic) is obtained from a pool of possible relevant documents »The pool is created by taking the top K documents (usually, K=100) in the rankings generated by various participating retrieval systems »The documents in the pool are then shown to human assessors who ultimately decide on the relevance of each document

18 18 The Tasks at the TREC Collection l Add hoc task l Routing task l TREC-6 »Chinese »Filtering »Interactive »NLP »Cross Languages »High precision »Spoken document »Very large corpus

19 19 Evaluation Measures at the TREC Conference l Summary table statistics »the number of topics, the number of relevant documents retrieved, l Recall-Precision Averages »11 standard recall levels l Document level averages »5, 10, 20, 100, R l Average precision histogram »R-precision


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