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Extracting Query Facets From Search Results

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. Outline. Introduction Approach Experiment Conclusion. What is query facet ?. Definition : query facet

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Extracting Query Facets From Search Results

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  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

  2. Outline • Introduction • Approach • Experiment • Conclusion

  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 ) a query facet (Mars rovers)

  4. What can we do with query facets ? • Flight type • Domestic • International • Travel Class • First • Business • Economy

  5. Goal • Extract query facets from the top-k web search results D={, , … , }

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

  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. • <ul> <li>first class</li> <li>business class</li> <li>economy class</li> </ul>

  8. Candidate lists • 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. • The candidate lists are usually noisy, and could be non-relevant to the issued query. • To address this problem, we use a supervised method.

  9. Note : What is Supervised Method Example : LA-100 LA-99(Training Data)

  10. Note : What is Supervised Learning Training data (with features) Training Model Prediction New Data Model

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

  12. Problem Definition • Whether a list item is a facet term • Whether a pair of list items is in one query facet

  13. Features

  14. Graph

  15. logistic-based conditional probability distributions

  16. Parameter Estimation Maximizing the log-likelihood using gradient descent.

  17. Inference • The training is finished. • The graphical model does not enforce the labeling to produce strict partitioning for facet terms. For example, when=1,=1, we may have = 0.

  18. Rephrase the optimization problem 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. 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.

  19. QF-I Select list items with as facet terms.

  20. QF-J

  21. Ranking Query Facets • score for a query facet : • score for a facet term :

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

  23. Data Using Top 10 query facets generated by different models.

  24. Evaluation Metrics • Using “∗” to distinguish between system generated results and human labeled results, which we used as ground truth.

  25. Clustering quality

  26. Overall quality fp-nDCG is weighted by rp-nDCGis weighted by

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

  28. Facet terms

  29. Clustering facet terms

  30. Overall

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

  32. Conclusion • 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.

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