Automatic query reformulation with syntactic operators to alleviate search difficulty
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Automatic Query Reformulation with Syntactic Operators to Alleviate Search Difficulty . Huizhong Duan, Rui Li, chengxiang Zhai University of illinois at urbana-champaign. Introduction. Search Engine No. 1 important tool for getting information. We use everyday. Queries

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Automatic Query Reformulation with Syntactic Operators to Alleviate Search Difficulty

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Automatic Query Reformulation with Syntactic Operators to Alleviate Search Difficulty

Huizhong Duan, Rui Li, chengxiangZhai

University of illinois at urbana-champaign


Introduction

  • Search Engine

    • No. 1 important tool for getting information.

    • We use everyday.

  • Queries

    • We are trained to use keyword queries.

  • Advanced Query Syntax

    • No idea what it is…


Advanced Query Syntax

  • Necessity Operator

    • E.g. green tree +street

    • I’m looking for a street!

  • Phrase Operator

    • E.g. “green tree street”

    • Not green street with trees!

  • Synonym Operator

    • E.g. green tree ~street

    • Hmm, I’m not sure it’s a street/road/avenue…

  • ……

  • Syntactic Operator, Syntactic Query, Syntactic Reformulation


Syntactic Operators

  • Extend our ability to express our information needs.

  • Potentially useful in formulating more effective queries.


Syntactic Operators


Syntactic Operators


Syntactic Operators

  • Are very effective if used appropriately.

  • Rarely used by ordinary users.

  • Difficult to use due to the lack of knowledge of the dataset.

  • Question: Can we automatically formulate syntactic queries given users’ keyword queries?


Problem formulation

  • Input:a keyword query q, a syntactic operator op and a target performance metric M.

  • Goal:to find a list of syntactic reformulations of q through the use of op:Sop(q)={q1,q2,…, qn| M(q1)>M(q2)>…>M(qn)}.

  • Tasks:

    • implicit refine: use q,q1,q2,…qmwith probabilities.

    • explicit refine: output top ranked query q1 if M’(q1)=M(q1)-M(q)>0, or otherwise the original query q.

    • diagnose query: users resort to help with an ineffective keyword query (negative / pseudo negative feedback is available)


The Model

  • Learning to rank

    • Learns a scoring function to score each sample

    • Pairwise or Listwise loss function

    • The score indicates the ranking

    • Score each candidate reformulation with the learned model

  • “green tree street”

  • “green tree” street

  • green “tree street”

  • green tree street


The features

  • Difficulty


The features

  • Distinguishability


The features

  • Negativity

  • Corresponds to a scenario where users resort to the reformulation only when they are not satisfied with the result from the keyword query

  • Negative feedback or pseudo negative feedback is available


Combining operators

  • Operator Combination

    • predict syntax queries with different operators jointly

  • Result-Combination

    • predict each operator separately and select the reformulation with the best predicted performance.


Experiments

  • Automatic reformulation: works for negative feedback scenario

  • Necessity operator: more useful for long queries

  • Phrase operator: more useful for short queries

  • Result-Combination: better than Operator-Combination

  • Syntactic reformulation: makes further improvement over existing negative feedback methods


Case studies

  • Discover representative keywords/phrases


Case studies

  • Discover undermatched concepts


Case studies

  • Eliminate ambiguities caused by matching keywords separately


conclusion

  • Automatic query reformulation through the use of query syntax operators

  • Formulate automatic syntactic reformulation as a supervised learning problem under the framework of learning to rank

  • Propose a set of effective features to represent the characteristics of syntax queries

  • Method is general, applicable to more syntactic operators


Questions


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