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Discovering Key Concepts in Verbose Queries

Discovering Key Concepts in Verbose Queries

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Discovering Key Concepts in Verbose Queries

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  1. Discovering Key Concepts in Verbose Queries Michael Bendersky and W. Bruce Croft University of Massachusetts SIGIR 2008

  2. Objective • “Discovering Key Concepts in Verbose Queries”

  3. 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

  4. 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

  5. Objective • “Discovering Key Concepts in Verbose Queries” • Use of key concepts?

  6. Objective • “Discovering Key Concepts in Verbose Queries” • Use of key concepts? • Combine with current IR model

  7. Retrieval Model • Conventional Language Model: score(q,d) = p(q|d) =

  8. Retrieval Model • Conventional Language Model: score(q,d) = p(q|d) = • New Model: score(q,d) = p(q|d) = =

  9. Final Retrieval Function score(q,d) =

  10. Final Retrieval Function score(q,d) = Language Model

  11. Final Retrieval Function score(q,d) = Key Concepts

  12. What is a Concept? • Noun phrase in a query

  13. 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

  14. 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

  15. Finding ‘Key’ Concepts • Rank concepts by p(ci|q)

  16. 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

  17. 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

  18. 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

  19. TREC Corpus

  20. 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

  21. Exp 1: Identifying Key Concept

  22. Exp 1: Identifying Key Concept • Better than idf ranking

  23. Exp 2: Information Retrieval score(q,d) = • Use only the top 2 concepts for each query • q is the entire <desc> section •  = 0.8

  24. Exp 2: Information Retrieval • KeyConcept[2]<desc> : author’s method • SeqDep<desc> : include all bigrams in query

  25. Exp 2: Information Retrieval

  26. What to take home? • Singling out key concepts improves retrieval