extracting query facets from search results

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Extracting Query Facets From Search ResultsDate : 2013/08/20Source : SIGIR’13Authors : Weize Kong and James AllanAdvisor : Dr.Jia-ling, KohSpeaker : Wei, Chang

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OUTLINE Introduction Approach Experiment Conclusion

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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)

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WHAT CAN WE DO WITH QUERY FACETS ?

• Flight type• Domestic• International

• Travel Class• First • Business• Economy

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GOAL Extract query facets from the top-k web

search results D={, , … , }

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OUTLINE Introduction Approach

Step 1 : Extracting candidate lists Step 2 : Finding query facets from candidate lists

Experiment Conclusion

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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>

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CANDIDATE LISTS

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.

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NOTE : WHAT IS SUPERVISED METHOD

Quiz 1 Quiz 2 Quiz 3 Final Exam

John A B+ B- BEric A+ A A+ APeter B+ A- A+ A+Steve A+ A+ B- B+Mark C A+ B+ BLarry B+ B+ B+ A

LA-99 (Training Data)

LA-100 Quiz 1 Quiz 2 Quiz 3 Final

ExamDavid A- B+ A- ?James B A A ?

EXAMPLE :

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NOTE : WHAT IS SUPERVISED LEARNING

TrainingTraining

data (with features)

Model

New Data Model Prediction

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OUTLINE Introduction Approach

Step 1 : Extracting candidate lists Step 2 : Finding query facets from candidate lists

Experiment Conclusion

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PROBLEM DEFINITION

Whether a list item is a facet term Whether a pair of list items is in one query

facet

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FEATURES

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GRAPH

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LOGISTIC-BASED CONDITIONAL PROBABILITY DISTRIBUTIONS

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PARAMETER ESTIMATION

Maximizing the log-likelihood using gradient descent.

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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.

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REPHRASE THE OPTIMIZATION PROBLEM

This optimization problem is NP-hard, which can be provedby 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.

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QF-I1. Select list items with as facet terms.2.

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QF-J

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RANKING QUERY FACETS

score for a query facet :

score for a facet term :

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OUTLINE Introduction Approach

Step 1 : Extracting candidate lists Step 2 : Finding query facets from candidate lists

Experiment Evaluation Experiment Result

Conclusion

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DATA

Using Top 10 query facets generated by different models.

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EVALUATION METRICS Using “∗” to distinguish between system

generated results and human labeled results, which we used as ground truth.

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CLUSTERING QUALITY

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OVERALL QUALITY

fp-nDCG is weighted by rp-nDCG is weighted by

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OUTLINE Introduction Approach

Step 1 : Extracting candidate lists Step 2 : Finding query facets from candidate lists

Experiment Evaluation Experiment Result

Conclusion

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FACET TERMS

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CLUSTERING FACET TERMS

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OVERALL

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OUTLINE Introduction Approach

Step 1 : Extracting candidate lists Step 2 : Finding query facets from candidate lists

Experiment Evaluation Experiment Result

Conclusion

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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.