1 / 21

Tuning Before Feedback: Combining Ranking Discovery and Blind Feedback for Robust Retrieval*

Tuning Before Feedback: Combining Ranking Discovery and Blind Feedback for Robust Retrieval*. Weiguo Fan, Ming Luo, Li Wang, Wensi Xi, and Edward A. Fox Digital Library Research Laboratory, Virginia Tech

trinh
Télécharger la présentation

Tuning Before Feedback: Combining Ranking Discovery and Blind Feedback for Robust Retrieval*

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Tuning Before Feedback:Combining Ranking Discovery andBlind Feedback for Robust Retrieval* Weiguo Fan, Ming Luo, Li Wang, Wensi Xi, and Edward A. Fox Digital Library Research Laboratory, Virginia Tech *This research is supported by the National Science Foundation under Grant Numbers IIS-0325579, DUE-0136690 and DUE-0333531

  2. Outline • Introduction • Research Questions • Approach: Ranking Tuning + Blind Fdbk • Experiment • Results • Conclusion

  3. Introduction • Ranking functions play an important role in IR performance • Blind feedback (pseudo-relevance feedback) has been found very useful for ad hoc retrieval • Why not combine ranking function optimization with blind feedback to improve robustness?

  4. Research Questions • Does blind feedback work even better on fine-tuned ranking functions as compared to on traditional ranking functions such as Okapi BM25? • Does the type of query (very short vs. very long) have any impact on the combination approach? • Can the ranking function discovered, in combination with blind feedback, extrapolate well for new unseen queries?

  5. Our Approach • Use ARRANGER • a Genetic Programming-based discovery engine • to perform the ranking function tuning • [Fan 2003tkde, Fan 2004ip&m, Fan 2004jasist] • Combine ranking tuning and feedback • Test on different types of queries

  6. Input Output Ranking Function Discovery Ranking Function f Feedback Training Data RF Discovery Problem

  7. Ranking Function Optimization • Ranking Function Tuning is an art! – Paul Kantor • Why not adaptively discover RF by Genetic Programming? • Huge search space • Discrete objective function • Modeling advantage • What is GP? • Problem solving system designed based on principles of evolution and heredity • Widely used for structure discovery, functional form discovery, other data mining and optimization tasks

  8. Genetic Algorithms/Programming • Representation: • Vector of bit strings or real numbers for GA • Complex data structures: trees, arrays for GP • Genetic transformation • Reproduction • Crossover • Mutation • IR application • [Gordon’88, ’91], [Chen’98a, ’98b], [Pathak’00], etc.

  9. Essential GP Components

  10. + * Parent2 Parent1 N/df+df df tf*(tf+df) / / Generation: N tf + df df N N Crossover tf df Generation: N+1 + * Child2 Child1 tf*(N/df) (tf*df)+df df tf + tf df Example of Crossover in GP

  11. Start Initialize Population 50 48 49 1 2 3 Evaluate Fitness 50 48 49 0.4 Apply Crossover 0.8 0.4 0.4 0.3 0.3 1 2 3 Stop? Validate and Output End The ARRANGER Engine • Split the training data into training and validation • Generate an initial population of random “ranking functions” • Evaluate the fitness of each “ranking function” in the population and record 10 best ones • If stopping criteria is not met, generate the next generation of population by genetic transformation, go to Step 3. • Validate the recorded best “ranking functions” and select the best one as the RF

  12. The ARRANGER Engine • Split the training data into training and validation • Generate an initial population of random “ranking functions” • Evaluate the fitness of each “ranking function” in the population and record 10 best ones • If stopping criteria is not met, generate the next generation of population by genetic transformation, go to Step 3. • Validate the recorded best “ranking functions” and select the best one as the RF

  13. Start Initialize Population 50 48 49 1 2 3 Evaluate Fitness 50 48 49 0.4 Apply Crossover 0.8 0.4 0.4 0.3 0.3 1 2 3 Stop? Validate and Output End The ARRANGER Engine

  14. Blind Feedback • Automatically adds more terms to a user’s query to enhance the performance of search engines by assuming top ranked docs relevant • Some examples • Rocchio (performs best in our experiment) • Dec-Hi • Kullback-Leibler Divergence (KLD) • Chi-Square

  15. An Integrated Model

  16. Experiment Setting • Data • 2003 Robust Track data (from TREC 6, 7, 8) • Training Queries • 150 old queries from TREC 6, 7, 8 • Test Questions • 50 very hard queries + 50 new queries

  17. The Results on 150 Training Queries

  18. Results on Test Queries (1)

  19. Results on Test Queries (2)

  20. Conclusions • Blind feedback works well on GP trained queries. • Ranking function combined with blind feedback works with new queries • Two stage model responds differently to Desc query (slightly better) and Long query

  21. Thank You! Q&A?

More Related