SIGIR 2011


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Summary of papers by Chetana Gavankar

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SIGIR 2011

  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. Types of 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)=∑fεI wf f(q) I = {Tsnippet(.),TTitle(.),TUrl(.)} Value each feature differently using wf 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. TSnippets = # Snippets Retrieved # Snippets Retrieved with Dates Identifying implicit temporal queries
  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 ➔Future dates common in snippets than query log ➔Query having dates does not necessarily mean that it has temporal intent (from web query logs of Google and yahoo) Ex: October Sky movie ➔Web snippets statistically more relevant in terms of temporal intent than query logs
  9. 9. Rishiraj Saha Roy, Niloy Ganguly, Monojit Choudhury, Naveen Singh: "Complex Network Analysis Reveals Kernel-Periphery Structure in Web Search Queries"
  10. 10. Search Queries Search Query language: bag of segments Word occurrence n/w: Edge exists if Pij > Pi Pj Eight complex network models for query logs ● Query Unrestricted wordnet(local) and (global) ● Query Restricted wordnet(local) and (global) ● Query Unrestricted SegmentNet(local) and (global) ● Query Restricted SegmentNet(local) and (global)
  11. 11. Kernel and Peripheral lexicons Two regimes in DD of word occurrence N/W: 1.Kernel lexicons (K-Lex or modifiers): • Units popular in query (high degrees) • Generic and domain independent 2.Peripheral lexicon (P-Lex or HEADs):Rare ones with degree much less than those in kernal P K-Lex (popular segments) P-Lex (rarer segments) how to matthew brodrick wiki accessories free police officer and who is in australia epson tx800 videos star trek next gen
  12. 12. Degree Disribution |N| = Nodes, |E| = edges C= average clustering coefficient d=mean shortest path between edges Crand and drand are corr. Values in random graph Crand ~ k'/ |N| , drand ~ ln(|N|)/ ln(|k'|) k'= average degree of graph Degree distribution= p(k) = nodes with degree k/ total nodes
  13. 13. Two regime power law
  14. 14. Conclusion ● Like NL, Queries reflect kernal-periphery distinction Unlike NL, Query N/W lack small word property for quickly retrieving words from mind ● More difficult to understand context of segment in query. ● Peripheral N/W consist of large number of small disconnected components ● Capability of peripheral units to exist by themselves makes POS identification hard in Queries. ● Socio-cultural factors govern the kernel-periphery distinction in queries
  15. 15. Lidong Bing, Wai Lam: "Investigation of Web Query Refinement via Topic Analysis and Learning with Personalization"
  16. 16. Web Query Refinement ● Query Refinement ● Substitution ● Expansion ● Deletion ● Stemming ● Spelling correction ● Abbreviation expansion ...................... ● Generate some candidate queries first, and score the quality of these candidates.
  17. 17. 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 topics zi Each zi is associated with multinomial distribution of terms P(tk|zi)= prob of term tk given topic zi
  18. 18. Personalization πu ={πu 1, πu 2, … , πu |z|} = profile of the user u, πu i = P(zi|u) = probability that the user u prefers the topic zi Generate user-based pseudo-document U for user u. {P(z1|U), P(z2|U), … , P(z|Z||U)} = profile of u. candidate query q: t1, … tn Topic of term tr = zr
  19. 19. Topic based scoring with personalization Candidate query score: model parameter P(zj|zi) captures the relationship of two topics With personal profile P(z1|u) = probability that user u prefers the topic z1
  20. 20. Conclusion Framework that considers personalization achieves the best performance. With user profiles, the topic-based scoring part is more reliable