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Threshold Calibration in Adaptive Filtering The CLARIT TREC-7 Filtering System

This study evaluates basic CLARIT adaptive filtering techniques with a focus on threshold setting. The experiment explores different algorithms for initial threshold setting and threshold updating, along with the impact of score bias and incomplete judgments on the threshold. The results show the effectiveness of beta-gamma regulation, increasing chunk sizes, and stopping delivery for difficult topics in improving filtering utility.

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Threshold Calibration in Adaptive Filtering The CLARIT TREC-7 Filtering System

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  1. Threshold Calibration in Adaptive FilteringThe CLARIT TREC-7 Filtering System Chengxiang Zhai, Peter Jansen, Emilia Stoica, Norbert Grot, David A. Evans CLARITECH Corporation A Justsystem Group Company

  2. Goal of Participation • Goal: To evaluate basic CLARIT adaptivefiltering techniques with emphasis on thresholdsetting • Six submissions • 2 for Adaptive F1 • 2 for Adaptive F3 • 2 for Batch filtering (F1 & F3) TREC-7 Filtering

  3. Basic CLARIT Filtering Approach • Phrase indexing (NPs, subterms, words) • Subdocument indexing • Dot product scoring Filtering = Retrieval + Routing + Threshold • Delivery-ratio initial threshold setting • Beta-gamma threshold updating • Variants of Rocchio • Modified probabilistic term weighting … ... … ... TREC-7 Filtering

  4. Threshold Setting Algorithms • Initial threshold setting: delivery-ratio • Approximate a given ratio of delivery • Threshold updating: beta-gamma regulation • Threshold varies between optimal utility point & zero utility point according to the number of training examples TREC-7 Filtering

  5. Delivery-Ratio ThresholdAlgorithm Profile Delivery-ratio = 0.2 = 1/5 D1 25.5 D2 23.2 … ... Dk 11.3 … Threshold = 11.3 Query Extrapolation needed ! Retrieve D1 25.5 D2 23.2 D3 0 D4 0 D1 25.5 d2 23.2 ... Ref corpus TREC-7 Filtering

  6. Often too high/tight! (“score bias” + incomplete judgments) Threshold varies in the range Utility Cutoff position 0 1 2 3 … K ...  Score-bias Influence of incomplete judgments (Confidence in ) Beta-Gamma Threshold Updating Threshold goes in this direction as more examples are available TREC-7 Filtering

  7. Experiment Design • Chunk-based simulation of real-time filtering • Increasing sizes: 3,000 docs, 4,000 docs, …,24,000 docs. • Delivery-ratio for initial threshold: 0.0005= 1/2000 • Beta-gamma threshold updating: beta=0.1, gamma=0.05 • Rocchio profile training for term vector updating (only term selection) • Batch filtering differs only in the initial term vector TREC-7 Filtering

  8. t1 t2 t3 ... ti ... IDF? WSJ87 AP88 AP89 Adaptive Filtering Experiment AP88 AP89 AP90 Results Profile TREC-7 Filtering

  9. Adaptive Filtering Results • Average utility per topic • Group comparison TREC-7 Filtering

  10. Three Main Positive Factors 1. Using beta-gamma ( vs. alpha ) regulation 2. Increasing chunk sizes ( vs. equal size ) 3. Stopping delivery for difficult topics ( for F1 ) TREC-7 Filtering

  11. Factor 1Beta-Gamma Regulation Beta-gamma regulation is better than alpha regulation TREC-7 Filtering

  12. Factor 2Increasing Chunk Sizes Increasing chunk sizes help learning TREC-7 Filtering

  13. Factor 3Stopping Delivery for Difficult Topics Clear advantage for F1 No advantage for F3 TREC-7 Filtering

  14. Conclusions & Further Work • Conclusions • Beta-gamma threshold regulation works very well. • Increasing chunk sizes help learning. • While not user-friendly, shutting off difficult topics improves F1 utility. • Further work • Improve beta-gamma threshold regulation • Study real-time/profile-specific updating • Exploit all history relevance information TREC-7 Filtering

  15. The End TREC-7 Filtering

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