Michael Bendersky , W. Bruce CroftDept. of Computer Science

Univ. of Massachusetts AmherstAmherst, MA

SIGIR 2012

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• Motivation• Query Hypergraphs• Ranking Documents• Parameter estimation• Evaluation• Conclusion

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Outline

Motivation• Goal : retrieve more relevant documents to

users• Query Representation :

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This paper

term dependencies

concept dependencies

bag-of-words

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),..}

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• ”Provide information on the use of dogs worldwide for law enforcement

purposes.”

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Example(cont.)

{provide, information,( law, enforcement)} {(dog, law enforcement)}

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

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Query Hypergraphs• Query Hypergraph

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(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

Query Hypergraph Induction

• Three types of structures

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• query term structure : individual query words • phrase structure : bi-gram (consider order)• proximity structure : arbitrary subsets of query terms

Hyperedges• Local hyperedges ({k},D)• Global hyperedge ( ,D)

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QK

k: a conceptQK : set of query concepts

k QK

Ranking Documents• relevance score

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Q: a queryD: a documente: a hyperedge E: set of hyperedges

Factor: )( ,Dkee

Local Factors

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)(k : the importance weight of the concept k

: a matching function between the concept k and the document D

Matching Function

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DCCktfDktf

Dkf

),(),(log),(

C: the collectionD

C

: the number of term in the document

: the number of term in the collection

: Dirichlet smoothing parameter

• consider the dependency between the entire set of query concepts

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Global Factor

: the highest score passage from the document

The dependency range is much longer for concept dependencies.

),( QKk : the importance weight of concept k in the context of the entire set of query concepts QK (with the concept in the passage )

Example

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{(dog, law enforcement)}

Don’t appear in the same sentence, but co-occurrence in a largertext passage.

Query Hypergraph Parameterization

• Goal: parameterize concept weights (local & global)

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)(k ),( QKk

• Parameterization By Structure• Parameterization By Concept

Parameterization By Structure

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: a structure

• parameterize the concept weights based on the concepts themselves

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Parameterization By Concept

concept importance feature

estimation

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

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)(

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Parameter Estimation(cont.)

Optimize the local component (the weight ))(k

retrieve top thousand documents

optimize the global component (the weight )),( QKk

Parameter Estimation(cont.)

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(Robust04 collection)

Evaluation(testing)• search engine - Indri • test collections

• query

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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)

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1

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))(1()( k

jj

k

i

i gRigR g= 0,1,2,3,4

R(g)=(2^g-1)/16

satisfied by doc k

not satisfied with previous doc (1~k-1)

Evaluation(retrieval performance)

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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.

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