Discovering key concepts in verbose queries
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Discovering Key Concepts in Verbose Queries. Michael Bendersky and W. Bruce Croft University of Massachusetts SIGIR 2008. Objective. “Discovering Key Concepts in Verbose Queries”. Objective. “Discovering Key Concepts in Verbose Queries” <num> Number 829 <title> Spanish Civil War support

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Discovering Key Concepts in Verbose Queries

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Discovering key concepts in verbose queries

Discovering Key Concepts in Verbose Queries

Michael Bendersky and W. Bruce Croft

University of Massachusetts

SIGIR 2008


Objective

Objective

  • “Discovering Key Concepts in Verbose Queries”


Objective1

Objective

  • “Discovering Key Concepts in Verbose Queries”

  • <num> Number 829

    <title> Spanish Civil War support

    <desc> Provide information on all kinds of material international support provided to either side in the Spanish Civil War


Objective2

Objective

  • “Discovering Key Concepts in Verbose Queries”

  • <num> Number 829

    <title> Spanish Civil War support

    <desc> Provide information on all kinds of material international support provided to either side in the Spanish Civil War


Objective3

Objective

  • “Discovering Key Concepts in Verbose Queries”

  • Use of key concepts?


Objective4

Objective

  • “Discovering Key Concepts in Verbose Queries”

  • Use of key concepts?

  • Combine with current IR model


Retrieval model

Retrieval Model

  • Conventional Language Model:

    score(q,d) = p(q|d) =


Retrieval model1

Retrieval Model

  • Conventional Language Model:

    score(q,d) = p(q|d) =

  • New Model:

    score(q,d) = p(q|d) = =


Final retrieval function

Final Retrieval Function

score(q,d) =


Final retrieval function1

Final Retrieval Function

score(q,d) =

Language Model


Final retrieval function2

Final Retrieval Function

score(q,d) =

Key Concepts


What is a concept

What is a Concept?

  • Noun phrase in a query


What is a concept1

What is a Concept?

  • Noun phrase in a query

  • <num> Number 829

    <title> Spanish Civil War support

    <desc> Provide information on all kinds of material international support provided to either side in the Spanish Civil War


What is a concept2

What is a Concept?

  • Noun phrase in a query

  • <num> Number 829

    <title> Spanish Civil War support

    <desc> Provide information on all kinds of material international support provided to either side in the Spanish Civil War


Finding key concepts

Finding ‘Key’ Concepts

  • Rank concepts by p(ci|q)


Finding key concepts1

Finding ‘Key’ Concepts

  • Rank concepts by p(ci|q)

  • Compute p(ci|q) by frequency?

  • <num> Number 829

    <title> Spanish Civil War support

    <desc> Provide information on all kinds of material international support provided to either side in the Spanish Civil War


Finding key concepts2

Finding ‘Key’ Concepts

  • Approximate p(ci|q) by machine learning

  • h(ci) is ci’s query-independent importance score

  • p(ci|q) = h(ci) / ciqh(ci)

AdaBoost.M1

h(ci)

ci


Features of a concept

Features of a Concept

  • is_cap : is capitalized

  • tf : in corpus

  • idf : in corpus

  • ridf : idf modified by Poisson model

  • wig : weighted information gain; change in entropy from corpus to retrieved data

  • g_tf : Google term frequency

  • qp : number of times the concept appears as a part of a query in MSN Live

  • qe : number of times the concept appears as exact query in MSN Live


Trec corpus

TREC Corpus


Exp 1 identifying key concept

Exp 1: Identifying Key Concept

  • Cross-validation on corpus

  • Each fold has 50 queries

  • Check whether the top concept is a key concept

  • Assume 1 key concept per query during annotation


Exp 1 identifying key concept1

Exp 1: Identifying Key Concept


Exp 1 identifying key concept2

Exp 1: Identifying Key Concept

  • Better than idf ranking


Exp 2 information retrieval

Exp 2: Information Retrieval

score(q,d) =

  • Use only the top 2 concepts for each query

  • q is the entire <desc> section

  •  = 0.8


Exp 2 information retrieval1

Exp 2: Information Retrieval

  • KeyConcept[2]<desc> : author’s method

  • SeqDep<desc> : include all bigrams in query


Exp 2 information retrieval2

Exp 2: Information Retrieval


What to take home

What to take home?

  • Singling out key concepts improves retrieval


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