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INFORMATION RETRIEVAL TECHNIQUES BY DR. ADNAN ABID

INFORMATION RETRIEVAL TECHNIQUES BY DR. ADNAN ABID. Lecture # 17 Processing a phrase query Proximity queries. ACKNOWLEDGEMENTS. The presentation of this lecture has been taken from the underline sources

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INFORMATION RETRIEVAL TECHNIQUES BY DR. ADNAN ABID

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  1. INFORMATION RETRIEVAL TECHNIQUESBYDR. ADNAN ABID Lecture # 17 Processing a phrase query Proximity queries

  2. ACKNOWLEDGEMENTS The presentation of this lecture has been taken from the underline sources • “Introduction to information retrieval” by PrabhakarRaghavan, Christopher D. Manning, and HinrichSchütze • “Managing gigabytes” by Ian H. Witten, ‎Alistair Moffat, ‎Timothy C. Bell • “Modern information retrieval” by Baeza-Yates Ricardo, ‎ • “Web Information Retrieval” by Stefano Ceri, ‎Alessandro Bozzon, ‎Marco Brambilla

  3. Outline • Processing a phrase query • Proximity queries • Combination schemes

  4. Processing a phrase query • Extract inverted index entries for each distinct term: to, be, or, not. • Merge their doc:position lists to enumerate all positions 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

  5. Proximity queries • LIMIT! /3 STATUTE /3 FEDERAL /2 TORT Here, /k means “within k words of”. • Clearly, positional indexes can be used for such queries; biword indexes cannot. • Exercise: Adapt the linear merge of postings to handle proximity queries. Can you make it work for any value of k?

  6. Positional index size • Can compress position values/offsets as we did with docs in the last lecture • Nevertheless, this expands postings storage substantially

  7. Postings Positional postings 1000 1 1 100,000 1 100 Document size Positional index size • Need an entry for each occurrence, not just once per document • Index size depends on average document size • Average web page has <1000 terms • SEC filings, books, even some epic poems … easily 100,000 terms • Consider a term with frequency 0.1% Why?

  8. Rules of thumb • Positional index size factor of 2-4 over non-positional index • Positional index size 35-50% of volume of original text • Caveat: all of this holds for “English-like” languages

  9. Combination schemes • A positional index expands postings storage substantially (Why?) • Biword indexes and positional indexes approaches can be profitably combined • For particular phrases (“Michael Jackson”, “Britney Spears”) it is inefficient to keep on merging positional postings lists • Even more so for phrases like “The Who”

  10. Wild Card Queries • Example • Stan*  Standard, Stanford • S*T  Start • *ion  Option • Pa*an  Pakistan • Pa*t*an etc…

  11. Resources for today’s lecture • MG 3.6, 4.3; MIR 7.2 • Porter’s stemmer: http//www.sims.berkeley.edu/~hearst/irbook/porter.html • H.E. Williams, J. Zobel, and D. Bahle, “Fast Phrase Querying with Combined Indexes”, ACM Transactions on Information Systems. http://www.seg.rmit.edu.au/research/research.php?author=4

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