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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

Automatic Query Reformulation with Syntactic Operators to Alleviate Search Difficulty

Huizhong Duan, Rui Li, chengxiangZhai

University of illinois at urbana-champaign

introduction
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
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
Syntactic Operators
  • Extend our ability to express our information needs.
  • Potentially useful in formulating more effective queries.
syntactic operators3
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
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
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
The features
  • Difficulty
the features1
The features
  • Distinguishability
the features2
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
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
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
Case studies
  • Discover representative keywords/phrases
case studies1
Case studies
  • Discover undermatched concepts
case studies2
Case studies
  • Eliminate ambiguities caused by matching keywords separately
conclusion
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
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