1. CS276A Text Information Retrieval, Mining, and Exploitation Lecture 10 7 Nov 2002

2. Information Access in Context Stop High-Level Goal Synthesize Done? Analyze yes no User Information Access

3. Exercise Observe your own information seeking behavior WWW University library Grocery store Are you a searcher or a browser? How do you reformulate your query? Read bad hits, then minus terms Read good hits, then plus terms Try a completely different query …

4. Correction: Address Field vs. Search Box Are users typing urls into the search box ignorant?.com /.org /.net / international urls cnn.com vs. www.cnn.comwww.cnn.com Full url with protocol qualifier vs. partial url

5. Today’s Topics Information design and visualization Evaluation measures and test collections Evaluation of interactive information retrieval Evaluation gotchas

6. Information Visualization and Exploration Tufte Shneiderman Information foraging: Xerox PARC / PARC Inc.

7. Edward Tufte Information design bible: The visual display of quantitative information The art and science of how to display (quantitative) information visually Significant influence on User Interface design

8. The Challenger Accident On January 28, 1986, the space shuttle Challenger explodes shortly after takeoff. Seven crew members die. One of the causes: an O ring failed due to cold temperatures. How could this happen?

9. How O-Rings were presented Time scale is shown – instead of temperature scale! “Needless junk” (rockets don’t show information) Graphic does not help answer question: why do o- rings fail?

10. Tufte: Principles for Information Design Omit needless junk Show what you mean Don't obscure the meaning and order of scales Make comparisons of related images possible Claim authorship, and think twice when others don't Seek truth

11. Tufte’s O-Ring Visualization

12. Tufte: Summary  “Like poor writing, bad graphical displays distort or obscure the data, make it harder to understand or compare, or otherwise thwart the communicative effect which the graph should convey.” Bad decisions are made based on bad information design. Tufte’s influence on UI design Examples of the best and worst in information visualization: http://www.math.yorku.ca/SCS/Gallery/nofr ames.html

13. Shneiderman: Information Visualization How to design user interfaces How to engineer user interfaces for software Task by type taxonomy

14. Shneiderman on HCI Well-designed interactive computer systems promote: Positive feelings of success, competence, and mastery. Allow users to concentrate on their work, rather than on the system. Marti Hearst

15. Task by Type Taxonomy: Data Types 1-D linear: seesoft 2-D map: multidimensional scaling (terms, docs, etc) 3-D world: cat-a-cone Multi-dim: table lens Temporal: topic detection Tree: hierarchies a la Yahoo Network: network graphs of sites (kartoo)

16. Task by Type Taxonomy: Tasks Overview: gain an overview of the entire collection Zoom: zoom in on items of interest Filter: filter out uninteresting items Details-on-demand: select an item or group and get details when needed Relate: view relationships among items History: keep a history of actions to support, undo, replay Extract: allow extraction of subcollections and the query parameters

17. Exercise If your project has a UI component: Which data types are being displayed? Which tasks are you supporting?

18. Xerox PARC: Information Foraging Metaphor from ecology/biology People looking for information = animals foraging for food Predictive model that allows principled way of designing user interfaces The main focus is: What will the user do next? How can we support a good choice for the next action? Rather than: Evaluation of a single user-system interaction

19. Foraging Paradigm Energy Food Foraging Biological, behavioral, and cultural designs are adaptive to the extent they optimize the rate of energy intake. George Robertson, Microsoft

20. Information Foraging Paradigm Information Information Foraging Information access and visualization technologies are adaptive to the extent they optimize the rate of gain of valuable information George Robertson, Microsoft

21. Searching Patches George Robertson, Microsoft

22. Information Foraging: Theory G – information/food gained g – average gain per doc/patch TB – total time between docs/patches tb – average time between docs/patches TW – total time within docs/patches tw – average time to process doc/patch lambda = 1/tb – prevalence of information/food

23. Information Foraging: Theory R = G / (TB + TW) – rate of gain R = lambda TB g / ( TB + lambda TB tw) R = lambda g / ( 1 + lambda tw) Goodness measure of UI = R = rate of gain Optimize UI by increasing R Increase prevalence lambda (asymptotic improvement) Decrease tw (time it takes to absorb doc/food) Better model: different types of docs/patches Model can be used to find optimal UI parameters

24. Cost-of-Knowledge Characteristic Function Improve productivity: Less time or more output Card, Pirolli, and Mackinlay

25. Creating Test Collections for IR Evaluation

26. Test Corpora

27. Kappa Measure Kappa measures Agreement among coders Designed for categorical judgments Corrects for chance agreement Kappa = [ P(A) – P(E) ] / [ 1 – P(E) ] P(A) – proportion of time coders agree P(E) – what agreement would be by chance Kappa = 0 for chance agreement, 1 for total agreement.

28. Kappa Measure: Example Number of docsJudge 1Judge 2 300Relevant 70Nonrelevant 20RelevantNonrelevant 10Nonrelevantrelevant P(A)? P(E)?

29. Kappa Example P(A) = 370/400 = 0.925 P(nonrelevant) = (10+20+70+70)/800 = 0.2125 P(relevant) = (10+20+300+300)/800 = 0.7878 P(E) = 0.2125^2 + 0.7878^2 = 0.665 Kappa = (0.925 – 0.665)/(1-0.665) = 0.776 For >2 judges: average pairwise kappas

30. Kappa Measure Kappa > 0.8 = good agreement 0.67 “tentative conclusions” (Carletta 96) Depends on purpose of study

31. Interjudge Disagreement: TREC 3

33. Impact of Interjudge Disagreement Impact on absolute performance measure can be significant (0.32 vs 0.39) Little impact on ranking of different systems or relative performance

34. Evaluation Measures

35. Recap: Precision/Recall Evaluation of ranked results: You can return any number of results ordered by similarity By taking various numbers of documents (levels of recall), you can produce a precision- recall curve Precision: #correct&retrieved/#retrieved Recall: #correct&retrieved/#correct The truth, the whole truth, and nothing but the truth. Recall 1.0 = the whole truth, precision 1.0 = nothing but the truth.

36. Recap: Precision-recall curves

37. F Measure F measure is the harmonic mean of precision and recall (strictly speaking F 1 ) 1/F = ½ (1/P + 1/R) Use F measure if you need to optimize a single measure that balances precision and recall.

38. F-Measure F1(0.956 ) = max = 0.96

39. Breakeven Point Breakeven point is the point where precision equals recall. Alternative single measure of IR effectiveness. How do you compute it?

40. Area under the ROC Curve True positive rate = recall = sensitivity False positive rate = fp/(tn+fp). Related to precision. fpr=0 p=1 Why is the blue line “worthless”?

41. Precision Recall Graph vs ROC

42. Unit of Evaluation We can compute precision, recall, F, and ROC curve for different units. Possible units Documents (most common) Facts (used in some TREC evaluations) Entities (e.g., car companies) May produce different results. Why?

43. Critique of Pure ReasonRelevance Relevance vs Marginal Relevance A document can be redundant even if it is highly relevant Duplicates The same information from different sources Marginal relevance is a better measure of utility for the user. Using facts/entities as evaluation units more directly measures true relevance. But harder to create evaluation set See Carbonell reference

44. Evaluation of Interactive Information Retrieval

45. Evaluating Interactive IR Evaluating interactive IR poses special challenges Obtaining experimental data is more expensive Experiments involving humans require careful design. Control for confounding variables Questionnaire to collect relevant subject data Ensure that experimental setup is close to intended real world scenario Approval for human subjects research

46. IIR Evaluation Case Study 1 TREC-6 interactive TREC report 9 participating groups (US, Europe, Australia) Control system (simple IR system) Each group ran their system and the control system 4 users at each site 6 queries (= topics) Goal of evaluation: Find best performing system Why do you need control system for comparing groups?

47. Queries (= Topics)

48. Latin Square Design

49. Analysis of Variance

52. Observations Query effect is largest std for each site High degree of query variability Searcher effect negligible for 4 our of 10 sites Best Model: Interactions are small compared too overall error. None of the 10 sites statistically better than control system! M1M2M3m4 #sites3421

53. IIR Evaluation Case Study 2 Evaluation of relevance feedback Koenemann & Belkin 1996

54. Why Evaluate Relevance Feedback?

55. Questions being Investigated Koenemann & Belkin 96 How well do users work with statistical ranking on full text? Does relevance feedback improve results? Is user control over operation of relevance feedback helpful? How do different levels of user control effect results? Credit: Marti Hearst

56. How much of the guts should the user see? Opaque (black box) (like web search engines) Transparent (see available terms after the r.f. ) Penetrable (see suggested terms before the r.f.) Which do you think worked best? Credit: Marti Hearst

58. Terms available for relevance feedback made visible (from Koenemann & Belkin) Credit: Marti Hearst

59. Details on User Study Koenemann & Belkin 96 Subjects have a tutorial session to learn the system Their goal is to keep modifying the query until they’ve developed one that gets high precision This is an example of a routing query (as opposed to ad hoc) Reweighting: They did not reweight query terms Instead, only term expansion pool all terms in rel docs take top N terms, where n = 3 + (number-marked-relevant-docs*2) (the more marked docs, the more terms added to the query) Credit: Marti Hearst

60. Details on User Study Koenemann & Belkin 96 64 novice searchers 43 female, 21 male, native English TREC test bed Wall Street Journal subset Two search topics Automobile Recalls Tobacco Advertising and the Young Relevance judgements from TREC and experimenter System was INQUERY (vector space with some bells and whistles) Credit: Marti Hearst

61. Sample TREC query Credit: Marti Hearst

62. Evaluation Precision at 30 documents Baseline: (Trial 1) How well does initial search go? One topic has more relevant docs than the other Experimental condition (Trial 2) Subjects get tutorial on relevance feedback Modify query in one of four modes no r.f., opaque, transparent, penetration Credit: Marti Hearst

63. Precision vs. RF condition (from Koenemann & Belkin 96) Credit: Marti Hearst Can we conclude from this chart that RF is better?

64. Effectiveness Results Subjects with R.F. did 17-34% better performance than no R.F. Subjects with penetration case did 15% better as a group than those in opaque and transparent cases. Credit: Marti Hearst

65. Number of iterations in formulating queries ( from Koenemann & Belkin 96) Credit: Marti Hearst

66. Number of terms in created queries (from Koenemann & Belkin 96) Credit: Marti Hearst

67. Behavior Results Search times approximately equal Precision increased in first few iterations Penetration case required fewer iterations to make a good query than transparent and opaque R.F. queries much longer but fewer terms in penetrable case -- users were more selective about which terms were added in. Credit: Marti Hearst

68. Evaluation Gotchas No statistical test (!) Lots of pairwise tests Wrong evaluation measure Query variability Unintentionally biased evaluation

69. Gotchas: Evaluation Measures KDD cup 2002 Optimize model parameter: balance factor Area under ROC curve and BEP have different behaviors These two measures intuitively measure the same property.

70. Gotchas: Query variability  Eichmann et al. claim that for their approach to CLIR French is harder than Spanish.  French average precision: 0.149  Spanish average precision: 0.173

71. Gotchas: Query variability  Queries with Spanish > baseline: 14  Queries with Spanish  baseline: 40  Queries with Spanish < baseline: 53  Queries with French > baseline: 20  Queries with French  baseline: 22  Queries with French < baseline: 64

72. Gotchas: Biased Evaluation Compare two IR algorithms 1. send query, present results 2. send query, cluster results, present clusters Experiment was simulated (no users) Results were clustered into 5 clusters Clusters were ranked according to percentage relevant documents Documents within clusters were ranked according to similarity to query

73. Sim-Ranked vs. Cluster-Ranked Does this show superiority of cluster ranking?

74. Relevance Density of Clusters

75. Summary Information Visualization: A good visualization is worth a thousand pictures. But to make information visualization work for text is hard. Evaluation Measures: F measure, break-even point, area under the ROC curve Evaluating interactive systems is harder than evaluating algorithms. Evaluation gotchas: Begin with the end in mind

76. Resources FOA 4.3 MIR Ch. 10.8 – 10.10 Ellen Voorhees, Variations in Relevance Judgments and the Measurement of Retrieval Effectiveness, ACM Sigir 98 Harman, D.K. Overview of the Third REtrieval Conference (TREC- 3). In: Overview of The Third Text REtrieval Conference (TREC-3). Harman, D.K. (Ed.). NIST Special Publication 500- 225, 1995, pp.l-19. "Assessing agreement on classification tasks: the kappa statistic", Jean Carletta, Computational Linguistics 22(2):249- 254, 1996 Reexamining the Cluster Hypothesis: Scatter/Gather on Retrieval Results (1996) Marti A. Hearst, Jan O. Pedersen Proceedings of SIGIR-96, http://gim.unmc.edu/dxtests/ROC3.htm Pirolli, P. and Card, S. K. (1999). Information Foraging. Psychological Review 106(4): 643-675. Paul Over, TREC-6 Interactive Track Report, NIST, 1998.

77. Resources http://www.acm.org/sigchi/chi96/proceedings/papers/Koenemann /jk1_txt.htm http://otal.umd.edu/olive Jaime Carbonell, Jade Goldstein, The use of MMR, diversity-based reranking for reordering documents and producing summaries, Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval, p.335-336, August 24-28, 1998, Melbourne, Australia