Summary of SIGIR 2011 Papers


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by Chetana Gavankar

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Summary of SIGIR 2011 Papers

  1. 1. Summary of Papers of SIGIR 2011 Workshop on Query Representation and Understanding Chetana Gavankar
  2. 2. Ricardo Campos, Alipio Jorge, Gael Dias: "Using Web Snippets and Query-logs to Measure Implicit Temporal Intents in Queries"
  3. 3. Temporal queries 1. Atemporal : Queries not sensitive to time like plan my trip 2. Temporal unambiguous : Queries in concrete time period. Ex : Haiti earthquake in 2010 3. Temporal ambiguous : queries with multiple instances over time. Ex : Cricket worldcup which occurs every four years.
  4. 4. Web snippets and Query Logs Content-Related Resources , based on a web content approach Simply requires the set of web search results. Query-Log Resources , based on similar year-qualified queries Imply that some versions of the query have already been issued.
  5. 5. 1. Web snippets ( temporal evidence within web pages): TA(q)= ∑ f ε I w f f(q) I = {Tsnippet(.),TTitle(.),TUrl(.)} Value each feature differently using w f 18.14 for TTitles, 50.91 for TSnippets and 30.95 for Turl(.) If TA(q) value < 10% then Atemporal. Dates appearing in query & docs may not match. # Snippets Retrieved with Dates Identifying implicit temporal queries TSnippets = # Snippets Retrieved
  6. 6. Identifying implicit temporal queries 2.Web Query Logs : Temporal activity can be recorded from date & time of request and from user activity. No. of times query is pre, post qualified by year is WA(q,y)=#(y,q) + #(q,y) α(q) = ∑ y WA (q,y) / ∑ x #(x,q) + ∑ x #(q,x) If query qualified with single year then α(q) =1
  7. 7. Results Temporal information is more frequent in web snippets than in any of the query logs of Google and Yahoo!; Most of the queries have a TSnippet(.) value around 20%, TLogYahoo(.) and TLogGoogle(.) are mostly near to 0%.
  8. 8. Conclusion <ul><li>Future dates common in snippets than query log
  9. 9. Query having dates does not necessarily mean that it has temporal intent (from web query logs of Google and yahoo) Ex: October Sky movie
  10. 10. Web snippets statistically more relevant in terms of temporal intent than query logs </li></ul>
  11. 11. Rishiraj Saha Roy, Niloy Ganguly, Monojit Choudhury, Naveen Singh: &quot;Complex Network Analysis Reveals Kernel-Periphery Structure in Web Search Queries&quot;
  12. 12. Search Queries Search Query language: bag of segments Word occurrence n/w: Edge exists if P ij > P i P j Eight complex network models for query logs <ul><li>Query Unrestricted wordnet(local) and (global)
  13. 13. Query Restricted wordnet(local) and (global)
  14. 14. Query Unrestricted SegmentNet(local) and (global)
  15. 15. Query Restricted SegmentNet(local) and (global) </li></ul>
  16. 16. Kernel and Peripheral lexicons Two regimes in DD of word occurrence N/W: 1.K ernel lexicons (K-Lex or modifiers): <ul><li>Units popular in query (high degrees)
  17. 17. Generic and domain independent
  18. 18. Ex: how to, wikipedia </li></ul>2.Peripheral lexicon (P-Lex or HEADs): Rare ones with degree much less than those in kernel Ex: Decision Tree algorithm
  19. 19. Degree Disribution |N| = Nodes, |E| = edges C= average clustering coefficient d=mean shortest path between edges C rand and d rand are corr. Values in random graph C rand ~ k'/ |N| , d rand ~ ln(|N|)/ ln(|k'|) k' = average degree of graph Degree distribution= p(k) = nodes with degree k/ total nodes
  20. 20. Two regime power law
  21. 21. Conclusion <ul><li>Like NL, Queries reflect kernal-periphery distinction
  22. 22. Unlike NL, Query N/W lack small word property for quickly retrieving words from mind
  23. 23. More difficult to understand context of segment in query.
  24. 24. Peripheral N/W consist of large number of small disconnected components
  25. 25. Capability of peripheral units to exist by themselves makes POS identification hard in Queries.
  26. 26. Socio-cultural factors govern the kernel-periphery distinction in queries </li></ul>
  27. 27. Lidong Bing, Wai Lam: &quot;Investigation of Web Query Refinement via Topic Analysis and Learning with Personalization&quot;
  28. 28. Web Query Refinement <ul><li>Query Refinement </li><ul><li>Substitution
  29. 29. Expansion
  30. 30. Deletion
  31. 31. Stemming
  32. 32. Spelling correction
  33. 33. Abbreviation expansion
  34. 34. ...................... </li></ul><li>Generate some candidate queries first, and score the quality of these candidates. </li></ul>
  35. 35. Latent Topic Analysis in Query Log Query log record (user_id, query, clicked_url, time) Pseudo-document generation: Queries related to the same host are aggregated. General sites like “” are not suitable for latent topic analysis & are eliminated Latent Dirichlet Allocation Algorithm) LDA to conduct the latent semantic topic analysis on the collection of host-based pseudo-documents. Z = set of latent topic s z i Each z i is associated with multinomial distribution of terms P ( tk | z i )= prob of term tk given topic z i
  36. 36. Personalization π u ={ π u 1 , π u 2 , … , π u |z| } = profile of the user u , π u i = P ( z i | u ) = probability that the user u prefers the topic z i Generate user-based pseudo-document U for user u . { P ( z 1 | U ), P ( z 2 | U ), … , P ( z | Z | | U )} = profile of u . candidate query q : t 1 , … t n Topic of term t r = z r
  37. 37. Topic based scoring with personalization Candidate query score: model parameter P ( zj | zi ) captures the relationship of two topics With personal profile P ( z 1 | u ) = probability that user u prefers the topic z 1
  38. 38. Conclusion Framework that considers personalization achieves the best performance. With user profiles, the topic-based scoring part is more reliable