G nd all web pages dealing with drug abuse classic

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Unformatted text preview: But we now need to deal with more than just equality Introduc)on to Informa)on Retrieval Sec. 2.4.2 Processing a phrase query §༊  Extract inverted index entries for each dis*nct term: to, be, or, not. §༊  Merge their doc:posi)on lists to enumerate all posi*ons with “to be or not to be”. §༊  to: §༊  2:1,17,74,222,551; 4:8,16,190,429,433; 7:13,23,191; ... §༊  be: §༊  1:17,19; 4:17,191,291,430,434; 5:14,19,101; ... §༊  Same general method for proximity searches Introduc)on to Informa)on Retrieval Sec. 2.4.2 Proximity queries §༊  LIMIT! /3 STATUTE /3 FEDERAL /2 TORT §༊  Again, here, /k means “within k words of”. §༊  Clearly, posi*onal indexes can be used for such queries; biword indexes cannot. §༊  Exercise: Adapt the linear merge of pos*ngs to handle proximity queries. Can you make it work for any value of k? §༊  This is a li_le tricky to do correctly and efficiently §༊  See Figure 2.12 of IIR Introduc)on to Informa)on Retrieval Sec. 2.4.2 Posi*onal...
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This document was uploaded on 02/14/2014.

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