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![Page 1: slides](https://reader034.fdocuments.in/reader034/viewer/2022042813/54bb3bc54a7959c05b8b465c/html5/thumbnails/1.jpg)
INFORMATION EXTRACTION FROM QUERIESEd Snelson, Joaquin Quiñonero Candela, Ralf Herbrich, Thore Graepel
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Information extraction from queries
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Templates
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Probabilistic query modelling
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Key details
EP message passing for inference within single query model
ADF single pass through queries Sparse messages within query Bootstrap from initial seed sets of
instances/attributes Directed processing of queries based on
current top beliefs
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Data
10 months, Live Search query logs 100 Million unique queries, with
associated counts Preliminary experiments on small
specific subsets e.g. 50,000 unique queries related to
actors, cars and national parks
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Seed lists
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Actors
Instances Attributes
tom cruise moviesbrad pitt picturesjohnny depp dealer.commatt damon photosgeorge clooney angelina joliecameron diaz nudescarlett johansson biographymel gibson newsgrand canyon heightsharon stone wedding
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Cars
Instances Attributes
dealer {Year}honda civic partshonda accord hybridford mustang dealerdodge charger usedtoyota camry worldford explorer accessoriestoyota corolla fordford focus cleveland plaindodge durango wachovia
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National Parks
Instances Attributes
grand canyon national parkyellowstone parkyosemite toursredwood lodgingdenali hotelseverglades lodgealgonquin westjoshua tree skywalkwest yellowstone gmcshenandoah college
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Templates
Templates
[Inst] [Attr][Attr] [Inst]{Year} [Inst] [Attr][Attr] of [Inst][Inst] and [Attr][Attr] and [Inst][Attr] in [Inst]the [Attr] [Inst]how [Attr] is [Inst][Attr] [Inst] coupe[Attr] [Inst] partsthe [Inst] [Attr][Inst] 's [Attr][Inst] in [Attr]
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Future improvements
Class/Attribute dependent templates A garbage class to deal with “noise” Reducing sensitivity to order of
processing initial queries Disambiguation, synonyms etc. Use of part-of-speech tagger Combination with standard hand-crafted
entity extraction techniques