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Entity Extraction for Query Interpretation Patrick Pantel ǂ

Entity Extraction for Query Interpretation Patrick Pantel ǂ. Query Representation and Understanding SIGIR July 23, 2010. Collaborators : Alpa Jain, Ana-Maria Popescu, Arkady Borkovsky , Eric Crestan, Hadar Shemtov, Marco Pennacchiotti, Nicolas Torzec, Vishnu Vyas

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Entity Extraction for Query Interpretation Patrick Pantel ǂ

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  1. Entity Extractionfor Query InterpretationPatrick Pantelǂ Query Representation and Understanding SIGIRJuly 23, 2010 Collaborators:Alpa Jain, Ana-Maria Popescu, Arkady Borkovsky, Eric Crestan, Hadar Shemtov, Marco Pennacchiotti, Nicolas Torzec, Vishnu Vyas ǂ Now at Microsoft Research

  2. The Ensemble Effect +19% average precision over state of the art (chart refers to class Athlete)

  3. Ensemble Semantics (Pennacchiotti and Pantel; EMNLP 2009)

  4. Ensemble Semantics (Pennacchiotti and Pantel; EMNLP 2009)

  5. Ensemble Semantics (Pennacchiotti and Pantel; EMNLP 2009)

  6. Ensemble Semantics (Pennacchiotti and Pantel; EMNLP 2009)

  7. Knowledge Extractors

  8. Pattern-based Extractor 1) Driving IDEA 2) Algorithm • State-of-the-art pattern-based algorithm for relation extraction [Pasca et al., 2006] • Instantiate typical relations of the class at hand • (e.g. act-in(Actor,Movie)) • How are Jubil Sarch and Kirov Chob related? • Jubil Sarch starring Kirov Chob • Jubil Sarch featuring the beautiful Kirov Chob • Steven Soderbergh-directed Jubil Sarch which earned star Kirov Chob an Oscar 3) In action Learn patterns Web Johnny Depp Denis Lawson Brad Pitt Morgan Freeman … Find new tuples Seed tuples act-in(Tom Hanks, The Terminal) act-in(Nicole Kidman, Eyes Wide Shut) Append reliable tuples

  9. Distributional Extractor 1) Driving IDEA 2) Algorithm • What is tezgüno? • A bottle of tezgünois on the table. • Everyone likes tezgüno. • Tezgünomakes you drunk. • We make tezgünoout of grapes. • State-of-the-art distributional entity extractor [Pantel et al., EMNLP 2009] • Given a small set of seeds for a given class, find distributionally similar candidate instances 3) In action rexhagon alexfong gene burke miguelhermosoarnao eikoando charlesmccaughan yukijirohotaru alecchristie dame wendyhiller john wayne arthur lake sir herbertbeerbohm tree tonyawright lorisaunders annagunn oliviergueritee tomas von bromssen harry jones judymatheson robertkeith mariaho'brien starring dennisquaid noah beery jr federicocastelluccio adienneshelly betty moran georgetakai joanneworley ruthhampton Nicole Kidman Al Pacino Tom Hanks Web

  10. Feature Generators

  11. Feature sets • 4 feature families • 5 feature types • 402 features Web 600M pages web crawl Query log 1 year of queries (top 1M) Web Table • From 600M pages web crawl Wikipedia 2M articles 2008 dump

  12. Query Log Features top-PMI attributes

  13. Web-table Features Harrison Ford Burt Lanacaster Ian Hart Nicholas Jones Denis Lawson

  14. Ranker

  15. EXPERIMENTS

  16. Experimental Setup • Task : Entity extraction • Tested classes : Actors, Athletes, Musicians • Gold Standard : - 500 manually annotated instances per class - 10 annotators, Kappa=0.88 • Evaluation :- Metric: average precision - 10-fold cross validation • Comparisons : (B1) pattern-based extractor (B2) distributional extractor (E1) combined extractor (E2) ML-combined extractor [Mirkin et al.,2006]

  17. Experimental Results • FEATURES • s = Source features • w = Webcrawl (600M docs) • q = Query Logs (1 year) • t = Web tables (600M docs) • k = Wikipedia

  18. Standalone Extractors gain : Actors

  19. Standalone Extractors gain : Athletes

  20. Standalone Extractors gain : Musicians

  21. Ensemble Effect: Athletes

  22. Takeaway: Harvesting knowledge requires different metrics than using the knowledge • Editorial cleaning of entities covering 80% of queries • Automatic cleaning of torso/tail • Impact on query and document Interpretation? 100 Most Frequent Entities in QL <10% Precision

  23. Conclusions • Ensemble Semantics: • Draw from many knowledge sources • Apply different extraction biases • Leverage many signals of knowledge • Significant gains in both coverage and precision on entity extraction • BUT, is the knowledge useful?? • 90% precision huge list of actors results in many many query interpretation errors! • Frequent terms tend to be ambiguous • Some highly accurate databases contain very bad errors (e.g., Texas and Baby are both actors in Y! Movies) • Further processing is necessary to make use of the knowledge…

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