Recomendados
Recomendados
Mais conteúdo relacionado
Semelhante a Reverted Indexing for Expansion and Feedback
Semelhante a Reverted Indexing for Expansion and Feedback (20)
Último
Último (20)
Reverted Indexing for Expansion and Feedback
- 1. Reverted Indexing for Feedback and Expansion Jeremy Pickens, Matthew Cooper, Gene Golovchinsky
- 2. Reverted Indexing for Feedback and Expansion Jeremy Pickens Catalyst Repository Systems
- 3. Query-Document Duality has long history • Using queries to label documents • Queries and documents as bipartite graph – Used for random walks – Used for partitioning • Reverse Querying
- 4. Motivation – Three R’s Retrievability Reuse (Algorithmic) Recall-Oriented Tasks
- 5. Our Key Contribution We treat query result sets as unstructured text “documents” -- and index them
- 6. Outline • Reverted Documents • Reverted Indexing • Experimental Setup • Results – Effectiveness – Efficiency • Related Work • Future Extensions
- 8. Basis Query (Reverted Document ID) Query Expression Ranking Algorithm giraffe BM25 cheetah BM25 gazelle BM25 gazelle Language Model gazelle PL2 (Divergence from Randomness) gazelle Y gazelle B gazelle G fast cheetah BM25 cheetah AND NOT gazelle Boolean Latitude+Longitude of Zanzibar Euclidean distance
- 9. Reverted Document Body Results (docid) Results (score) Canonical URL and/or docid 1. Probability of Relevance 2. Cosine similarity 3. KL Divergence 4. Raw Rank 5. 1 or 0 (Boolean)
- 10. rank docid score shift-scale Ahn&Moffat 1 #415 0.82 10.0 10 2 #32 0.73 8.92 9 3 #63 0.62 7.57 8 4 #7 0.49 5.95 6 5 #56 0.35 4.24 4 6 #12 0.14 1.72 2 7 #108 0.12 1.36 1 8 #115 0.09 1.09 1 9 #42 0.08 1.0 1 10 #85 0.08 1.0 1 Result Set→Document Body
- 11. Result Set→Document Body docid Ahn&Moffat #415 10 #32 9 #63 8 #7 6 #56 4 #12 2 #108 1 #115 1 #42 1 #85 1 <text> 415 415 415 415 415 415 415 415 415 415 32 32 32 32 32 32 32 32 32 63 63 63 63 63 63 63 63 7 7 7 7 7 7 56 56 56 56 12 12 108 115 42 85 </text>
- 13. Reverted Document <document> <docid> [gazelle : BM25] </docid> <text> 415 415 415 415 415 415 415 415 415 415 32 32 32 32 32 32 32 32 32 63 63 63 63 63 63 63 63 7 7 7 7 7 7 56 56 56 56 12 12 108 115 42 85 </text> </document>
- 14. Questions?
- 15. Outline • Reverted Documents • Reverted Indexing • Experimental Setup • Results – Effectiveness – Efficiency • Related Work • Future Extensions
- 16. Reverted Indexing 1. Choose a set of basis queries 2. For each basis query: 1. Execute each query, producing results up to cutoff depth k 2. Use results to create a “reverted document” 3. Add the reverted document to the index How basis queries are chosen (in these experiments): All singleton terms (unigrams) with df ≥ 2. Ranking algorithm for all basis queries is PL2.
- 17. Standard Index
- 18. Reverted Index
- 20. Reverted Index Statistics Retrieval Score of docid Term Frequency Sum of Retrieval Scores of all docids retrieved by a Basis Query Document Length Number of Basis Queries that docid was retrieved by Document Frequency
- 21. Outline • Reverted Documents • Reverted Indexing • Experimental Setup • Results – Effectiveness – Efficiency • Related Work • Future Extensions
- 22. Experiment: Relevance Feedback 1. Run initial query using PL2 (Terrier platform) [poaching wildlife preserves] 2. Judge top k documents for relevance 3. 4. Expand using top 500 terms (strongest baseline @ 500) 5. Run expanded query using PL2 6. Evaluate Use KL Divergence to select and weight query expansion terms Use Bo1 to select and weight query expansion terms Use PL2 retrieval on the Reverted Index to select and weight query expansion terms
- 23. Reverted Index→Expansion 1. Original query = [poaching wildlife preserves] 2. Reverted query = [#415 #56 #42 #85] 3. Expanded query = [poaching^2.0 wildlife^1.24 preserves^1.0 poachers^0.57 tsavo^0.56 leakey^0.41 tusks^0.39 …] term original retrieved weight poaching 1 1.0 2.0 poachers 0 0.57 0.57 tsavo 0 0.56 0.56 leakey 0 0.41 0.41 tusks 0 0.39 0.39 elephants 0 0.34 0.34 wildlife 1 0.24 1.24 kws 0 0.2 0.2 … … … … preserves 1 0 1.0
- 24. Outline • Reverted Documents • Reverted Indexing • Experimental Setup • Results – Effectiveness – Efficiency • Related Work • Future Extensions
- 25. MAP
- 26. %Change
- 27. Residual MAP
- 28. %Change
- 29. Efficiency • Two components to query expansion – Selection and Weighting – Execution of Expanded Query
- 32. Why would execution be faster?
- 33. Bo1 Reverted_PL2 Term Score Term Score leakey 0.88 poaching 1.00 poaching 0.74 poachers 0.56 wildlife 0.73 tsavo 0.56 kenya 0.52 leakey 0.41 ivory 0.47 tusks 0.39 elephants 0.46 elephants 0.34 elephant 0.32 wildlife 0.24 deer 0.30 kws 0.20 poachers 0.28 kez 0.17 conservation 0.27 ivory 0.14 species 0.23 jealousies 0.14 tusks 0.19 elephant 0.14 african 0.19 conservationists 0.09 namibia 0.19 kenya 0.09 animals 0.17 fiefdom 0.08 africa 0.15 safaris 0.04 zimbabwe 0.15 conservationist 0.03 tsavo 0.14 egos 0.01 kenyan 0.13 kierie 0.00 conservationists 0.12 aphrodisiacs 0.00
- 34. Bo1 Reverted_PL2 Term DF Term DF africa 20390 wildlife 2891 african 10636 kenya 1163 conservation 4298 ivory 1014 animals 3928 elephant 743 species 3479 elephants 356 wildlife 2891 poaching 331 kenya 1163 conservationists 293 ivory 1014 egos 269 zimbabwe 966 kez 173 deer 748 fiefdom 129 elephant 743 conservationist 125 namibia 483 poachers 117 kenyan 436 safaris 57 elephants 356 jealousies 56 poaching 331 tusks 42 conservationists 293 leakey 22 poachers 117 tsavo 12 tusks 42 aphrodisiacs 12 leakey 22 kws 9 tsavo 12 kierie 2 Average DF 2617 Average DF 391
- 35. Bo1 Reverted_PL2 Term DF Term DF los 46748 transportation 15262 angeles 45147 freeway 3506 metro 39849 tunnel 2643 safety 22569 disasters 1822 fire 21257 subway 805 foot 13120 extinguished 452 traffic 12410 rtd 227 feet 12034 caved 193 hollywood 7677 shoring 158 heat 6004 roper 147 rail 5747 timbers 98 downtown 5390 shored 97 engineers 4308 pilgrimages 73 freeway 3506 asphyxiation 71 disasters 1822 smolder 29 firefighters 1489 busway 22 subway 805 grouting 21 rtd 227 smoldered 19 timbers 98 lutgen 10 busway 22 droped 2 Average DF 12511 Average DF 1283
- 36. Outline • Reverted Documents • Reverted Indexing • Experimental Setup • Results – Effectiveness – Efficiency • Related Work • Future Extensions
- 37. Related Work Inspiration: “Retrievability: An Evaluation Measure for Higher Order Information Access Tasks” -- Azzopardi and Vinay, CIKM 2008 Azzopardi & Vinay take a document centric approach, examining whether documents (n)ever appear among top k results to any query
- 38. Related Work Query-Document Duality has long history – S. E. Robertson. “Query-Document Symmetry and Dual models.” Journal of Documentation, 50(3),1994 – B. Billerbeck, F. Scholer, H. E. Williams, and J. Zobel. Query Expansion Using Associated Queries. CIKM '03 – N. Craswell and M. Szummer. Random walks on the Query-Click Graph. SIGIR 2007 – Reverse Querying / alerting (various)
- 39. Future Extensions Basis queries – Query expression may be arbitrarily complex – Ranking function may be arbitrarily complex (remember: ranking function is a part of the basis query) Reverted queries – Best Match: [#415 #56 #42 #85] – Boolean: (#415 AND #56) OR (#42 AND #85) – Other query operators: [SYNONYM(#415 #56) #42 #85] [ORDERED(#415 #56) #42 #85]
- 40. Motivation – Three R’s Retrievability Reuse (Algorithmic) Recall-Oriented Tasks
- 41. Questions?
Notas do Editor
- My main difference: TF (=original basis query retrieval score, i.e. it’s tied to the actual performance of the system) and IDF (=just how many basis queries a docid was retrieved by). Other notes: Craswell: bi-partite click-thru graphs for random walks (manual selection no automatic retrievability) model aggregate behavior using random walks from single starting document (no notion of indexing collection) Billerbeck et al Build pseudo-documents comprised of previous queries (text only) Limited to user queries Truncates result sets; degenerate DL and IDF statistics No relevance scores Standard text search and query expansion in these pseudo-documents Just under 25% of documents in the collection had zero associations “Reverse Querying” / alerting Given a document(s?), find the queries that match it In implementations I’ve seen, this is just a Boolean proposition (matches/doesn’t match), either as a whole or in the top-k. Even if it’s in the top-k, it’s a boolean presence/absense No sense of “tf”, or of “idf”
- Docids become “terms”. Score becomes “term frequency”.
- And that’s it. We’re done. In IR, we know how to go forward from here!
- Add each reverted document to the index.. All-the-while calculating global and local statistics, e.g. tf and idf, etc.
- Why we call this a “reverted” index.
- PL2 as the “forward” ranking algorithm, because we determined a priori that yielded the best MAP…and we want as many relevant docs in the top k as possible. Note that everything else is held constant, except for the term expansion and weighting
- My main difference: TF (=original basis query retrieval score, i.e. it’s tied to the actual performance of the system) and IDF (=just how many basis queries a docid was retrieved by). Other notes: Craswell: bi-partite click-thru graphs for random walks (manual selection no automatic retrievability) model aggregate behavior using random walks from single starting document (no notion of indexing collection) Billerbeck et al Build pseudo-documents comprised of previous queries (text only) Limited to user queries Truncates result sets; degenerate DL and IDF statistics No relevance scores Standard text search and query expansion in these pseudo-documents Just under 25% of documents in the collection had zero associations “Reverse Querying” / alerting Given a document(s?), find the queries that match it In implementations I’ve seen, this is just a Boolean proposition (matches/doesn’t match), either as a whole or in the top-k. Even if it’s in the top-k, it’s a boolean presence/absense No sense of “tf”, or of “idf”
- There are many similarities with running docids as queries, to running terms as queries. But they’re not completely similar! (phrase operators, for example?)
- (Retrievability): The basis queries that we retrieve already have shown themselves as capable of retrieving (at least some) relevant documents at a high rank! Don’t need to build fancy probabilistic models to know what the “best” terms are, because the basis queries have already “recorded” it. … (Reuse): Did not have to invent any new relevance feedback models. Simply used PL2 (or BM25 or tf.idf or LM or DFRee) to do the retrieval of basis queries. Combining terms, synonym operators, etc. all possible … (Recall): Evaluated by applying it to relevance-feedback
- And that’s it. We’re done. In IR, we know how to go forward from here!