1. Extracting Query Facets From Search Results Date : 2013/08/20 Source : SIGIR’13 Authors : Weize Kong and James Allan Advisor : Dr.Jia-ling, Koh Speaker : Wei, Chang 1

2. O UTLINE Introduction Approach Experiment Conclusion 2

3. What is query facet ? Definition : query facet a set of coordinate terms ( terms that share a semantic relationship by being grouped under a relationship ) 3 a query facet (Mars rovers)

4. W HAT CAN WE DO WITH QUERY FACETS ? 4 Flight type Domestic International Travel Class First Business Economy

5. G OAL 5

6. O UTLINE Introduction Approach Step 1 : Extracting candidate lists Step 2 : Finding query facets from candidate lists Experiment Conclusion 6

7. PATTERN - BASED SEMANTIC CLASS EXTRACTION Reference from : Z. Dou, S. Hu, Y. Luo, R. Song, and J.-R. Wen. Finding dimensions for queries. For example : There are many Mars rovers, such as Curiosity, Opportunity, and Spirit. first class business class economy class 7

8. C ANDIDATE LISTS 8 The candidate lists are usually noisy, and could be non-relevant to the issued query. To address this problem, we use a supervised method. All the list items are normalized by converting text to lowercase and removing non-alphanumeric characters. Then, we remove stopwords and duplicate items in each lists. Finally, we discard all lists that contain fewer than two item or more than 200 items.

9. N OTE : W HAT IS S UPERVISED M ETHOD 9 Quiz 1Quiz 2Quiz 3Final Exam JohnAB+B-B EricA+A A PeterB+A-A+ SteveA+ B-B+ MarkCA+B+B LarryB+ A LA-99 (Training Data) LA-100 Quiz 1Quiz 2Quiz 3Final Exam DavidA-B+A- ? JamesBAA ? E XAMPLE :

10. N OTE : W HAT IS S UPERVISED L EARNING 10 Training Training data (with features) Model New Data Model Prediction

11. O UTLINE Introduction Approach Step 1 : Extracting candidate lists Step 2 : Finding query facets from candidate lists Experiment Conclusion 11

12. P ROBLEM D EFINITION 12 Whether a list item is a facet term Whether a pair of list items is in one query facet

13. F EATURES 13

14. G RAPH 14

15. LOGISTIC - BASED CONDITIONAL PROBABILITY DISTRIBUTIONS 15

16. P ARAMETER E STIMATION 16 Maximizing the log-likelihood using gradient descent.

17. I NFERENCE 17

18. R EPHRASE THE OPTIMIZATION PROBLEM 18 This optimization problem is NP-hard, which can be proved by a reduction from the Multiway Cut problem. Therefore, we propose two algorithms, QF-I and QF-J, to approximate the results. The optimization target becomes, where is the set of all possible query facet sets that can be generated from L with the strict partitioning constraint.

19. QF-I 19

20. QF-J 20

21. R ANKING Q UERY F ACETS score for a query facet : score for a facet term : 21

22. O UTLINE Introduction Approach Step 1 : Extracting candidate lists Step 2 : Finding query facets from candidate lists Experiment Evaluation Experiment Result Conclusion 22

23. D ATA 23 Using Top 10 query facets generated by different models.

24. E VALUATION M ETRICS Using “ ∗ ” to distinguish between system generated results and human labeled results, which we used as ground truth. 24

25. C LUSTERING QUALITY 25

26. O VERALL QUALITY 26 fp-nDCG is weighted by rp-nDCG is weighted by

27. O UTLINE Introduction Approach Step 1 : Extracting candidate lists Step 2 : Finding query facets from candidate lists Experiment Evaluation Experiment Result Conclusion 27

28. F ACET TERMS 28

29. C LUSTERING FACET TERMS 29

30. O VERALL 30

31. O UTLINE Introduction Approach Step 1 : Extracting candidate lists Step 2 : Finding query facets from candidate lists Experiment Evaluation Experiment Result Conclusion 31

32. C ONCLUSION We developed a supervised method based on a graphical model to recognize query facets from the noisy facet candidate lists extracted from the top ranked search results. We proposed two algorithms for approximate inference on the graphical model. We designed a new evaluation metric for this task to combine recall and precision of facet terms with grouping quality. Experimental results showed that the supervised method significantly outperforms other unsupervised methods, suggesting that query facet extraction can be effectively learned. 32