Upcoming SlideShare
×

# Query operations

• 267 views

More in: Technology
• Comment goes here.
Are you sure you want to
Your message goes here
Be the first to comment
Be the first to like this

Total Views
267
On Slideshare
0
From Embeds
0
Number of Embeds
0

Shares
11
0
Likes
0

No embeds

### Report content

No notes for slide

### Transcript

• 1. Query Operations Part 0ne Dr Ali Al-Ibrahim
• 2. 2 Reformulation of Query Manual • Add or remove search terms • Change Boolean operators Automatic • Remove search terms • Change weighting of search terms • Add new search terms
• 3. 3 Relevance Feedback: Document Vectors as Points on a Surface • Normalize all document vectors to be of length 1 • Then the ends of the vectors all lie on a surface with unit radius • For similar documents, we can represent parts of this surface as a flat region • Similar document are represented as points that are close together on this surface
• 4. 4 Results of a Search x x x x x x x ∆ hits from search x documents found by search ∆ query
• 5. 5 Relevance Feedback (Concept( x x x x o o o ∆ hits from original search x documents identified as non-relevant o documents identified as relevant ∆ original query reformulated query
• 6. 6 Theoretically Best Query x x x x o o o optimal query x non-relevant documents o relevant documents o o o x x x x x x x x xx x x x x
• 7. 7 Theoretically Best Query For a specific query, Q, let: DR be the set of all relevant documents DN-R be the set of all non-relevant documents sim(Q, DR) be the mean similarity between query Q and documents in DR sim(Q, DN-R) be the mean similarity between query Q and documents in DN-R The theoretically best query would maximize: F = sim(Q, DR) - sim(Q, DN-R)
• 8. 8 Estimating the Best Query In practice, DR and DN-R are not known. (The objective is to find them.) However, the results of an initial query can be used to estimate sim(Q, DR) and sim(Q, DN-R).
• 9. 9 Rocchio's Modified Query Modified query vector = Original query vector + Mean of relevant documents found by original query - Mean of non-relevant documents found by original query
• 10. 10 Query Modification Q1 = Q0 + Ri - SiΣ i =1 n1 n1 1 Σ i =1 n2 n2 1 Q0 = vector for the initial query Q1 = vector for the modified query Ri = vector for relevant document i Si = vector for non-relevant document i n1 = number of relevant documents n2 = number of non-relevant documents Rocchio 1971
• 11. 11 Difficulties with Relevance Feedback x x x x o o o ∆ optimal query x non-relevant documents o relevant documents ∆ original query reformulated query o o o x x x x x x x x xx x x x x Hits from the initial query are contained in the gray shaded area
• 12. 12 Difficulties with Relevance Feedback x x x x o o o ∆ optimal results set x non-relevant documents o relevant documents ∆ original query reformulated query o o o x x x x x x x x xx x x x x What region provides the optimal results set?
• 13. 13 When to Use Relevance Feedback Relevance feedback is most important when the user wishes to increase recall, i.e., it is important to find all relevant documents. Under these circumstances, users can be expected to put effort into searching: • Formulate queries thoughtfully with many terms • Review results carefully to provide feedback • Iterate several times • Combine automatic query enhancement with studies of thesauruses and other manual enhancements
• 14. 14 Adjusting Parameters 1: Relevance Feedback Q1 = α Q0 + β Ri - γ SiΣ i =1 n1 n1 1 Σ i =1 n2 n2 1 α, β and γ are weights that adjust the importance of the three vectors. If γ = 0, the weights provide positive feedback, by emphasizing the relevant documents in the initial set. If β = 0, the weights provide negative feedback, by reducing the emphasis on the non-relevant documents in the initial set.
• 15. 15 Adjusting Parameters 2:Filtering Incoming Messages D1, D2, D3, ... is a stream of incoming documents that are to be divided into two sets: R - documents judged relevant to an information need S - documents judged not relevant to the information need A query is defined as the vector in the term vector space: Q = (w1, w2, ..., wn) where wi is the weight given to term i Dj will be assigned to R if similarity(Q, Dj) > λ
• 16. Query Operations Chapter 5 Part Two
• 17. 17 Operations  Introduction  Relevance Feedback  Query Expansion  Term Reweighting  Automatic Local Analysis  Query Expansion using Clustering  Automatic Global Analysis  Query Expansion using Thesaurus  Similarity Thesaurus  Statistical Thesaurua
• 18. 18 Query Operations Introduction  IR queries as stated by the user may not be precise or effective.  There are many techniques to improve a stated query and then process that query instead.
• 19. 19 Relevance Feedback  Use assessments by users as to the relevance of previously returned documents to create new (modify old) queries.  Technique: 1. Increase weights of terms from relevant documents. 2. Decrease weight of terms from non-relevant documents.
• 20. 20 Relevance Feedback  After initial retrieval results are presented, allow the user to provide feedback on the relevance of one or more of the retrieved documents.  Use this feedback information to reformulate the query.  Produce new results based on reformulated query.  Allows more interactive.
• 21. 21 Relevance Feedback Architecture Rankings IR System Document corpus Ranked Documents 1. Doc1 2. Doc2 3. Doc3 . . 1. Doc1 ⇓ 2. Doc2 ⇑ 3. Doc3 ⇓ . . Feedback Query String Revise d Query ReRanked Documents 1. Doc2 2. Doc4 3. Doc5 . . Query Reformulation
• 22. 22 Query Reformulation  Revise query to account for feedback: Query Expansion: Add new terms to query from relevant documents. Term Reweighting: Increase weight of terms in relevant documents and decrease weight of terms in irrelevant documents.  Several algorithms for query reformulation.
• 23. 23 Query Reformulation for VSR  Change query vector using vector algebra.  Add the vectors for the relevant documents to the query vector.  Subtract the vectors for the irrelevant docs from the query vector.  This adds both positive and negatively weighted terms to the query as well as reweighting the initial terms.
• 24. 24 Optimal Query  Assume that the relevant set of documents Cr are known.  Then the best query that ranks all and only the relevant queries at the top is: ∑∑ ∉∀∈∀ − −= rjrj Cd j rCd j r opt d CN d C q   11 Where N is the total number of documents.
• 25. 25 Standard Rochio Method  Since all relevant documents unknown, just use the known relevant (Dr) and irrelevant (Dn) sets of documents and include the initial query q. ∑∑ ∈∀∈∀ −+= njrj Dd j nDd j r m d D d D qq   γβ α α: Tunable weight for initial query. β: Tunable weight for relevant documents. γ: Tunable weight for irrelevant documents.
• 26. 26
• 27. 27 Ide Regular Method  Since more feedback should perhaps increase the degree of reformulation, do not normalize for amount of feedback: ∑∑ ∈∀∈∀ −+= njrj Dd j Dd jm ddqq   γβα α: Tunable weight for initial query. β: Tunable weight for relevant documents. γ: Tunable weight for irrelevant documents.
• 28. 28 Ide “Dec Hi” Method  Bias towards rejecting just the highest ranked of the irrelevant documents: )(max jrelevantnon Dd jm ddqq rj   − ∈∀ −+= ∑ γβα α: Tunable weight for initial query. β: Tunable weight for relevant documents. γ: Tunable weight for irrelevant document.
• 29. 29 Comparison of Methods  Overall, experimental results indicate no clear preference for any one of the specific methods.  All methods generally improve retrieval performance (recall & precision) with feedback.  Generally just let tunable constants equal 1.
• 30. 30 Why is Feedback Not Widely Used?  Users sometimes reluctant to provide explicit feedback.  Results in long queries that require more computation to retrieve, and search engines process lots of queries and allow little time for each one.  Makes it harder to understand why a particular document was retrieved.
• 31. 31 Pseudo Feedback  Use relevance feedback methods without explicit user input.  Just assume the top m retrieved documents are relevant, and use them to reformulate the query.  Allows for query expansion that includes terms that are correlated with the query terms.
• 32. 32 Pseudo Feedback Results  Found to improve performance on TREC competition ad-hoc retrieval task.  Works even better if top documents must also satisfy additional boolean constraints in order to be used in feedback.
• 33. 33
• 34. 34 Local vs. Global Automatic Analysis  Local – Documents retrieved are examined to automatically determine query expansion. No relevance feedback needed.  Global – Thesaurus used to help select terms for expansion.
• 35. 35 Thesaurus  A thesaurus provides information on synonyms and semantically related words and phrases.  Example: physician syn: ||croaker, doc, doctor, MD, medical, mediciner, medico, ||sawbones rel: medic, general practitioner, surgeon,
• 36. 36 Thesaurus-based Query Expansion  For each term, t, in a query, expand the query with synonyms and related words of t from the thesaurus.  May weight added terms less than original query terms.  Generally increases recall.  May significantly decrease precision, particularly with ambiguous terms.  “interest rate” → “interest rate fascinate evaluate”
• 37. 37 Statistical Thesaurus  Existing human-developed thesauri are not easily available in all languages.  Human thesauri are limited in the type and range of synonymy and semantic relations they represent.  Semantically related terms can be discovered from statistical analysis of corpora.
• 38. 38 Query Expansion Based on a Statistical Thesaurus  Global thesaurus is composed of classes which group correlated terms in the context of the whole collection  Such correlated terms can then be used to expand the original user query  This terms must be low frequency terms  However, it is difficult to cluster low frequency terms  To circumvent this problem, we cluster documents into classes instead and use the low frequency terms in these documents to define our thesaurus classes.  This algorithm must produce small and tight clusters.
• 39. 39 Automatic Local Analysis  At query time, dynamically determine similar terms based on analysis of top-ranked retrieved documents.  Base correlation analysis on only the “local” set of retrieved documents for a specific query.  Avoids ambiguity by determining similar (correlated) terms only within relevant documents.  “Apple computer” → “Apple computer Powerbook laptop”
• 40. 40 Automatic Local Analysis  Expand query with terms found in local clusters.  Dl – set of documents retireved for query q.  Vl – Set of words used in Dl.  Sl – Set of distinct stems in Vl.  fsi,j –Frequency of stem si in document dj found in Dl.  Construct stem-stem association matrix.
• 41. 41 Association Matrix w1 w2 w3 …………………..wn w1 w2 w3 . . wn c11 c12 c13…………………c1n c21 c31 . . cn1 cij: Correlation factor between stems si and stem sj k l ij sik sjk d D c f f ∈ = ×∑ fik : Frequency of term i in document k
• 42. 42 Problems with Local Analysis  Term ambiguity may introduce irrelevant statistically correlated terms.  “Apple computer” → “Apple red fruit computer”  Since terms are highly correlated anyway, expansion may not retrieve many additional documents.
• 43. 43 Automatic Global Analysis  Determine term similarity through a pre- computed statistical analysis of the complete corpus.  Compute association matrices which quantify term correlations in terms of how frequently they co-occur.  Expand queries with statistically most similar terms.
• 44. 44 Automatic Global Analysis  There are two modern variants based on a thesaurus-like structure built using all documents in collection Query Expansion based on a Similarity Thesaurus Query Expansion based on a Statistical Thesaurus
• 45. 45 Global vs. Local Analysis  Global analysis requires intensive term correlation computation only once at system development time.  Local analysis requires intensive term correlation computation for every query at run time (although number of terms and documents is less than in global analysis).  But local analysis gives better results.
• 46. 46 Query Expansion Conclusions  Expansion of queries with related terms can improve performance, particularly recall.  However, must select similar terms very carefully to avoid problems, such as loss of precision.
• 47. 47 Conclusion  Thesaurus is a efficient method to expand queries  The computation is expensive but it is executed only once  Query expansion based on similarity thesaurus may use high term frequency to expand the query  Query expansion based on statistical thesaurus need well defined parameters