1. Michael Bendersky, W. Bruce Croft Dept. of Computer Science Univ. of Massachusetts Amherst Amherst, MA SIGIR 2012 1

2. Motivation Query Hypergraphs Ranking Documents Parameter estimation Evaluation Conclusion 2 Outline

3. Motivation Goal : retrieve more relevant documents to users Query Representation : 3 This paper term dependencies concept dependencies bag-of-words

4. Example ”Provide information on the use of dogs worldwide for law enforcement purposes.” bag-of-word { Provide, information, dog….} term dependency {(Provide, information ),( law, enforcement)} concept dependency {(dog, law enforcement),..} 4

5. ”Provide information on the use of dogs worldwide for law enforcement purposes.” 5 Example (cont.) {provide, information,( law, enforcement)} {(dog, law enforcement)}

6. Model concept dependency Use Query Hypergraphs 1. build linguistic structure ” members of the rock group nirvana” 2. each element in the structures can be represented as a concept 6

7. Query Hypergraphs Query Hypergraph 7 (international art crime) D: a document V = {D,i,a,c,ac} E = {({i},D),({a},D),({c},D),({ac},D),({i,a,c,ac},D)} hyperedge

8. Query Hypergraph Induction Three types of structures 8 query term structure : individual query words phrase structure : bi-gram (consider order) proximity structure : arbitrary subsets of query terms

9. Hyperedges Local hyperedges ({k},D) Global hyperedge (,D) 9 k: a concept : set of query concepts k

10. Ranking Documents relevance score 10 Q: a query D: a document e: a hyperedge E: set of hyperedges Factor:

11. Local Factors 11 : the importance weight of the concept k : a matching function between the concept k and the document D

12. Matching Function 12 C: the collection : the number of term in the document : the number of term in the collection : Dirichlet smoothing parameter

13. consider the dependency between the entire set of query concepts 13 Global Factor : the highest score passage from the document The dependency range is much longer for concept dependencies. : the importance weight of concept k in the context of the entire set of query concepts (with the concept in the passage )

14. Example 14 {(dog, law enforcement)} Don’t appear in the same sentence, but co-occurrence in a larger text passage.

15. Query Hypergraph Parameterization Goal: parameterize concept weights (local & global) 15 Parameterization By Structure Parameterization By Concept

16. Parameterization By Structure 16 : a structure

17. parameterize the concept weights based on the concepts themselves 17 Parameterization By Concept concept importance feature estimation

18. Parameter Estimation optimize a target metric (mean average precision) rely on a large collection use coordinate ascent algorithm - a coordinate-level hill climbing search repeatedly cycles through each of parameters, while holding all other parameters fixed 18

19. 19 Parameter Estimation (cont.) Optimize the local component (the weight ) retrieve top thousand documents optimize the global component (the weight )

20. Parameter Estimation (cont.) 20 (Robust04 collection)

21. Evaluation (testing) search engine - Indri test collections query 21

22. Evaluation (evaluation metric) MAP(mean average precision) ex. Topic 1 : 3 個相關 (order: 1,3,5) (1/1+2/3+3/5)/3 ERR@k (expected reciprocal rank, k=20) 22 g= 0,1,2,3,4 R(g)=(2^g-1)/16 satisfied by doc k not satisfied with previous doc (1~k-1)

23. Evaluation (retrieval performance) 23

24. Conclusion model arbitrary term dependencies as concepts uses passage-level evidence to model the dependencies between the concepts assign weight to both concepts and concept dependencies The proposed retrieval framework improves the retrieval effectiveness for verbose natural queries. 24