CS47300: Web Information Search and Management

Transcription

1 CS47300: Web Informaton Search and Management Prof. Chrs Clfton 15 September 2017 Materal adapted from course created by Dr. Luo S, now leadng Albaba research group Retreval Models Informaton Need Representaton Representaton Query Retreval Model Indexed Objects Retreved Objects Evaluaton/Feedback Jan Chrstopher W. Clfton 1

2 Query Expanson: Query: ran raq war Intal Retreval Result /11/88, Japan Ad to Buy Gear For Shps n Persan Gulf /21/90, Iraq's Not-So-Tough Army /10/90, Socete Generale Iran Pact /11/88, South Korea Estmates Iran-Iraq Buldng Orders /02/92, Internatonal: Iran Seeks Ad for War Damage /09/86, Army Suspends Frngs Of TOWs Due to Problems New query representaton: Query Expanson: Iran 9.54 raq 6.53 war 2.3 army 3.3 persan 1.2 ad 1.5 gulf 1.8 raegan 1.02 shp 1.61 troop 1.2 mltary 1.1 damage Jan Chrstopher W. Clfton 2

3 Updated Query Refned Retreval Result Query Expanson: /21/90, Iraq's Not-So-Tough Army /02/92, Internatonal: Iran Seeks Ad for War Damage /11/88, Japan Ad to Buy Gear For Shps n Persan Gulf /10/90, Socete Generale Iran Pact /11/88, South Korea Estmates Iran-Iraq Buldng Orders /05/87, Reagan to Urge Alles at Vence Summt To Endorse Cease-Fre n Iran-Iraq War Two types of words are lkely to be ncluded n the expanded query Topc specfc words: good representatve words General words: ntroduce ambguty nto the query, may lead to degradaton of the retreval performance Utlze both postve and negatve to dstngush representatve words Jan Chrstopher W. Clfton 3

4 Goal: Move new query close to relevant and far away from rrelevant Approach: New query s a weghted average of orgnal query, and relevant and non-relevant document vectors q = Ԧq + α 1 R d β 1 NR d (Roccho formula) d R d NR Postve feedback for terms n relevant docs Relevant Irrelevant Negatve feedback for terms n rrelevant docs Goal: Move new query close to relevant and far away from rrelevant Approach: New query s a weghted average of orgnal query, and relevant and non-relevant document vectors q = Ԧq + α 1 R d β 1 NR d (Roccho formula) d R d NR How do we set the desred weghts? Jan Chrstopher W. Clfton 4

5 Desrable weghts for α and β Exhaustve search Heurstc choce α=0.5; β=0.25 Learnng method Perceptron algorthm (Roccho) Support Vector Machne (SVM) Regresson Neural network algorthm Desrable weghts for α and β Intal Query Relevant Documents Try fnd and such that New Query q(, ) d 1 for d R q(, ) d 1 for d NR Irrelevant Documents Jan Chrstopher W. Clfton 5

6 Blnd (Pseudo) What f users only mark some relevant? Use bottom as negatve What f users only mark some rrelevant? Use top n ntal ranked lsts and queres as postve What f users do not provde any relevance judgments? Use top n ntal ranked lsts as postve ; bottom as negatve What about mplct feedback? Use readng tme, scrollng and other nteracton? 66 Blnd (Pseudo) Approaches Pseudo-relevance feedback Assume top N (e.g., 20) n ntal lst are relevant Assume bottom N (e.g., ) n ntal lst are rrelevant Calculate weghts of term accordng to some crteron (e.g., Roccho) Select top M (e.g., 10) terms Local context analyss Smlar approach to pseudo-relevance feedback But use passages nstead of for ntal retreval; use dfferent term weght selecton algorthms Jan Chrstopher W. Clfton 6

7 Summary Relevance feedback can be very effectve Effectveness depends on the number of judged (postve more mportant) An area of actve research (many open questons) Effectveness also depends on the qualty of ntal retreval results (what about bad ntal results?) Need to do retreval process twce Summary: Query Expanson Add terms to query to mprove recall And possbly precson Query Expanson va External Resources Thesaurus Industral Chemcal Thesaurus, Medcal Subject Headngs (MeSH) Semantc network WordNet Use user-specfed good to get new terms Blnd/Pseudo Roccho Jan Chrstopher W. Clfton 7