1. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 1 龙星计划课程 : 信息检索 Overview of Text Retrieval: Part 1 ChengXiang Zhai ( 翟成祥 ) Department of Computer Science Graduate School of Library & Information Science Institute for Genomic Biology, Statistics University of Illinois, Urbana-Champaign http://www-faculty.cs.uiuc.edu/~czhai, czhai@cs.uiuc.edu

2. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 2 Outline Basic Concepts in TR Evaluation of TR Common Components of a TR system Vector Space Retrieval Model

3. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 3 Basic Concepts in TR

4. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 4 What is Text Retrieval (TR)? There exists a collection of text documents User gives a query to express the information need A retrieval system returns relevant documents to users Known as “search technology” in industry

5. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 5 TR vs. Database Retrieval Information –Unstructured/free text vs. structured data –Ambiguous vs. well-defined semantics Query –Ambiguous vs. well-defined semantics –Incomplete vs. complete specification Answers –Relevant documents vs. matched records TR is an empirically defined problem!

6. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 6 TR is Hard! Under/over-specified query –Ambiguous: “buying CDs” (money or music?) –Incomplete: what kind of CDs? –What if “CD” is never mentioned in document? Vague semantics of documents –Ambiguity: e.g., word-sense, structural –Incomplete: Inferences required Even hard for people! –80% agreement in human judgments

7. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 7 TR is “Easy”! TR CAN be easy in a particular case –Ambiguity in query/document is RELATIVE to the database –So, if the query is SPECIFIC enough, just one keyword may get all the relevant documents PERCEIVED TR performance is usually better than the actual performance –Users can NOT judge the completeness of an answer

8. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 8 History of TR on One Slide Birth of TR –1945: V. Bush’s article “As we may think” –1957: H. P. Luhn’s idea of word counting and matching Indexing & Evaluation Methodology (1960’s) –Smart system (G. Salton’s group) –Cranfield test collection (C. Cleverdon’s group) –Indexing: automatic can be as good as manual (controlled vocabulary) TR Models (1970’s & 1980’s) … Large-scale Evaluation & Applications (1990’s-Present) –TREC (D. Harman & E. Voorhees, NIST) –Web search, PubMed, … –Boundary with related areas are disappearing

9. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 9 Short vs. Long Term Info Need Short-term information need (Ad hoc retrieval) –“Temporary need”, e.g., info about used cars –Information source is relatively static –User “pulls” information –Application example: library search, Web search Long-term information need (Filtering) –“Stable need”, e.g., new data mining algorithms –Information source is dynamic –System “pushes” information to user –Applications: news filter

10. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 10 Importance of Ad hoc Retrieval Directly manages any existing large collection of information There are many many “ad hoc” information needs A long-term information need can be satisfied through frequent ad hoc retrieval Basic techniques of ad hoc retrieval can be used for filtering and other “non-retrieval” tasks, such as automatic summarization.

11. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 11 Formal Formulation of TR Vocabulary V={w 1, w 2, …, w N } of language Query q = q 1,…,q m, where q i  V Document d i = d i1,…,d im i, where d ij  V Collection C= {d 1, …, d k } Set of relevant documents R(q)  C –Generally unknown and user-dependent –Query is a “hint” on which doc is in R(q) Task = compute R’(q), an “approximate R(q)”

12. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 12 Computing R(q) Strategy 1: Document selection –R(q)={d  C|f(d,q)=1}, where f(d,q)  {0,1} is an indicator function or classifier –System must decide if a doc is relevant or not (“absolute relevance”) Strategy 2: Document ranking –R(q) = {d  C|f(d,q)>  }, where f(d,q)  is a relevance measure function;  is a cutoff –System must decide if one doc is more likely to be relevant than another (“relative relevance”)

13. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 13 Document Selection vs. Ranking + + + + - - - - - - - - - - - - - - + - - Doc Selection f(d,q)=? + + + + - - + - + - - - - - - - - Doc Ranking f(d,q)=? 1 0 0.98 d 1 + 0.95 d 2 + 0.83 d 3 - 0.80 d 4 + 0.76 d 5 - 0.56 d 6 - 0.34 d 7 - 0.21 d 8 + 0.21 d 9 - R’(q) True R(q)

14. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 14 Problems of Doc Selection The classifier is unlikely accurate –“Over-constrained” query (terms are too specific): no relevant documents found –“Under-constrained” query (terms are too general): over delivery –It is extremely hard to find the right position between these two extremes Even if it is accurate, all relevant documents are not equally relevant Relevance is a matter of degree!

15. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 15 Ranking is often preferred Relevance is a matter of degree A user can stop browsing anywhere, so the boundary is controlled by the user –High recall users would view more items –High precision users would view only a few Theoretical justification: Probability Ranking Principle [Robertson 77]

16. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 16 Probability Ranking Principle [Robertson 77] As stated by Cooper Robertson provides two formal justifications Assumptions: Independent relevance and sequential browsing (not necessarily all hold in reality) “If a reference retrieval system’s response to each request is a ranking of the documents in the collections in order of decreasing probability of usefulness to the user who submitted the request, where the probabilities are estimated as accurately a possible on the basis of whatever data made available to the system for this purpose, then the overall effectiveness of the system to its users will be the best that is obtainable on the basis of that data.”

17. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 17 According to the PRP, all we need is “A relevance measure function f” which satisfies For all q, d 1, d 2, f(q,d 1 ) > f(q,d 2 ) iff p(Rel|q,d 1 ) >p(Rel|q,d 2 ) Most IR research has focused on finding a good function f

18. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 18 Evaluation in Information Retrieval

19. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 19 Evaluation Criteria Effectiveness/Accuracy –Precision, Recall Efficiency –Space and time complexity Usability –How useful for real user tasks?

20. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 20 Methodology: Cranfield Tradition Laboratory testing of system components –Precision, Recall –Comparative testing Test collections –Set of documents –Set of questions –Relevance judgments

21. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 21 The Contingency Table Relevant Retrieved Irrelevant RetrievedIrrelevant Rejected Relevant Rejected Relevant Not relevant RetrievedNot Retrieved Doc Action

22. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 22 How to measure a ranking? Compute the precision at every recall point Plot a precision-recall (PR) curve precision recall x x x x precision recall x x x x Which is better?

23. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 23 Summarize a Ranking: MAP Given that n docs are retrieved –Compute the precision (at rank) where each (new) relevant document is retrieved => p(1),…,p(k), if we have k rel. docs –E.g., if the first rel. doc is at the 2 nd rank, then p(1)=1/2. –If a relevant document never gets retrieved, we assume the precision corresponding to that rel. doc to be zero Compute the average over all the relevant documents –Average precision = (p(1)+…p(k))/k This gives us (non-interpolated) average precision, which captures both precision and recall and is sensitive to the rank of each relevant document Mean Average Precisions (MAP) – MAP = arithmetic mean average precision over a set of topics –gMAP = geometric mean average precision over a set of topics (more affected by difficult topics)

24. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 24 Summarize a Ranking: NDCG What if relevance judgments are in a scale of [1,r]? r>2 Cumulative Gain (CG) at rank n –Let the ratings of the n documents be r1, r2, …rn (in ranked order) –CG = r1+r2+…rn Discounted Cumulative Gain (DCG) at rank n –DCG = r1 + r2/log 2 2 + r3/log 2 3 + … rn/log 2 n –We may use any base for the logarithm, e.g., base=b –For rank positions above b, do not discount Normalized Cumulative Gain (NDCG) at rank n –Normalize DCG at rank n by the DCG value at rank n of the ideal ranking –The ideal ranking would first return the documents with the highest relevance level, then the next highest relevance level, etc – Compute the precision (at rank) where each (new) relevant document is retrieved => p(1),…,p(k), if we have k rel. docs NDCG is now quite popular in evaluating Web search

25. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 25 When There’s only 1 Relevant Document Scenarios: –known-item search –navigational queries Search Length = Rank of the answer: –measures a user’s effort Mean Reciprocal Rank (MRR): –Reciprocal Rank: 1/Rank-of-the-answer –Take an average over all the queries

26. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 26 Precion-Recall Curve Mean Avg. Precision (MAP) Recall=3212/4728 Breakeven Point (prec=recall) Out of 4728 rel docs, we’ve got 3212 D1 + D2 + D3 – D4 – D5 + D6 - Total # rel docs = 4 System returns 6 docs Average Prec = (1/1+2/2+3/5+0)/4 about 5.5 docs in the top 10 docs are relevant Precision@10docs

27. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 27 What Query Averaging Hides Slide from Doug Oard’s presentation, originally from Ellen Voorhees’ presentation

28. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 28 The Pooling Strategy When the test collection is very large, it’s impossible to completely judge all the documents TREC’s strategy: pooling –Appropriate for relative comparison of different systems –Given N systems, take top-K from the result of each, combine them to form a “pool” –Users judge all the documents in the pool; unjudged documents are assumed to be non-relevant Advantage: less human effort Potential problem: –bias due to incomplete judgments (okay for relative comparison) –Favor a system contributing to the pool, but when reused, a new system’s performance may be under-estimated Reuse the data set with caution!

29. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 29 User Studies Limitations of Cranfield evaluation strategy: –How do we evaluate a technique for improving the interface of a search engine? –How do we evaluate the overall utility of a system? User studies are needed General user study procedure: –Experimental systems are developed –Subjects are recruited as users –Variation can be in the system or the users –Users use the system and user behavior is logged –User information is collected (before: background, after: experience with the system) Clickthrough-based real-time user studies: –Assume clicked documents to be relevant –Mix results from multiple methods and compare their clickthroughs

30. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 30 Common Components in a TR System

31. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 31 Typical TR System Architecture User querydocs results Query Rep Doc Rep (Index) Scorer Indexer Tokenizer Index judgments Feedback

32. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 32 Text Representation/Indexing Making it easier to match a query with a document Query and document should be represented using the same units/terms Controlled vocabulary vs. full text indexing Full-text indexing is more practically useful and has proven to be as effective as manual indexing with controlled vocabulary

33. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 33 What is a good indexing term? Specific (phrases) or general (single word)? Luhn found that words with middle frequency are most useful –Not too specific (low utility, but still useful!) –Not too general (lack of discrimination, stop words) –Stop word removal is common, but rare words are kept All words or a (controlled) subset? When term weighting is used, it is a matter of weighting not selecting of indexing terms (more later)

34. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 34 Tokenization Word segmentation is needed for some languages –Is it really needed? Normalize lexical units: Words with similar meanings should be mapped to the same indexing term –Stemming: Mapping all inflectional forms of words to the same root form, e.g. computer -> compute computation -> compute computing -> compute (but king->k?) –Are we losing finer-granularity discrimination? Stop word removal –What is a stop word? What about a query like “to be or not to be”?

35. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 35 Relevance Feedback Updated query Feedback Judgments: d 1 + d 2 - d 3 + … d k -... Query Retrieval Engine Results: d 1 3.5 d 2 2.4 … d k 0.5... User Document collection

36. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 36 Pseudo/Blind/Automatic Feedback Query Retrieval Engine Results: d 1 3.5 d 2 2.4 … d k 0.5... Judgments: d 1 + d 2 + d 3 + … d k -... Document collection Feedback Updated query top 10

37. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 37 What You Should Know How TR is different from DB retrieval Why ranking is generally preferred to document selection (justified by PRP) How to compute the major evaluation measure (precision, recall, precision-recall curve, MAP, gMAP, breakeven precision, NDCG, MRR) What is pooling What is tokenization (word segmentation, stemming, stop word removal) What is relevance feedback; what is pseudo relevance feedback

38. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 38 Overview of Retrieval Models Relevance  (Rep(q), Rep(d)) Similarity P(r=1|q,d) r  {0,1} Probability of Relevance P(d  q) or P(q  d) Probabilistic inference Different rep & similarity Vector space model (Salton et al., 75) Prob. distr. model (Wong & Yao, 89) … Generative Model Regression Model (Fox 83) Classical prob. Model (Robertson & Sparck Jones, 76) Doc generation Query generation LM approach (Ponte & Croft, 98) (Lafferty & Zhai, 01a) Prob. concept space model (Wong & Yao, 95) Different inference system Inference network model (Turtle & Croft, 91) Learn to Rank (Joachims 02) (Burges et al. 05)

39. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 39 Retrieval Models: Vector Space

40. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 40 The Basic Question Given a query, how do we know if document A is more relevant than B? One Possible Answer If document A uses more query words than document B (Word usage in document A is more similar to that in query)

41. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 41 Relevance = Similarity Assumptions –Query and document are represented similarly –A query can be regarded as a “document” –Relevance(d,q)  similarity(d,q) R(q) = {d  C|f(d,q)>  }, f(q,d)=  (Rep(q), Rep(d)) Key issues –How to represent query/document? –How to define the similarity measure  ?

42. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 42 Vector Space Model Represent a doc/query by a term vector –Term: basic concept, e.g., word or phrase –Each term defines one dimension –N terms define a high-dimensional space –Element of vector corresponds to term weight –E.g., d=(x 1,…,x N ), x i is “importance” of term i Measure relevance by the distance between the query vector and document vector in the vector space

43. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 43 VS Model: illustration Java Microsoft Starbucks D6D6 D 10 D9D9 D4D4 D7D7 D8D8 D5D5 D 11 D2D2 ? D1D1 ? ?? ? D3D3 Query

44. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 44 What the VS model doesn’t say How to define/select the “basic concept” –Concepts are assumed to be orthogonal How to assign weights –Weight in query indicates importance of term –Weight in doc indicates how well the term characterizes the doc How to define the similarity/distance measure

45. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 45 What’s a good “basic concept”? Orthogonal –Linearly independent basis vectors –“Non-overlapping” in meaning No ambiguity Weights can be assigned automatically and hopefully accurately Many possibilities: Words, stemmed words, phrases, “latent concept”, …

46. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 46 How to Assign Weights? Very very important! Why weighting –Query side: Not all terms are equally important –Doc side: Some terms carry more information about contents How? –Two basic heuristics TF (Term Frequency) = Within-doc-frequency IDF (Inverse Document Frequency) –TF normalization

47. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 47 TF Weighting Idea: A term is more important if it occurs more frequently in a document Some formulas: Let f(t,d) be the frequency count of term t in doc d –Raw TF: TF(t,d) = f(t,d) –Log TF: TF(t,d)=log f(t,d) –Maximum frequency normalization: TF(t,d) = 0.5 +0.5*f(t,d)/MaxFreq(d) –“ Okapi/BM25 TF ” : TF(t,d) = k f(t,d)/(f(t,d)+k(1-b+b*doclen/avgdoclen)) Normalization of TF is very important!

48. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 48 TF Normalization Why? –Document length variation –“Repeated occurrences” are less informative than the “first occurrence” Two views of document length –A doc is long because it uses more words –A doc is long because it has more contents Generally penalize long doc, but avoid over- penalizing (pivoted normalization)

49. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 49 TF Normalization (cont.) Norm. TF Raw TF Which curve is more reasonable? Should normalized-TF be up-bounded? Normalization interacts with the similarity measure

50. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 50 Regularized/“Pivoted” Length Normalization Norm. TF Raw TF “Pivoted normalization”: Using avg. doc length to regularize normalization 1-b+b*doclen/avgdoclen (b varies from 0 to 1) What would happen if doclen is {>, <,=} avgdoclen? Advantage: stabalize parameter setting

51. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 51 IDF Weighting Idea: A term is more discriminative if it occurs only in fewer documents Formula: IDF(t) = 1+ log(n/k) n – total number of docs k -- # docs with term t (doc freq)

52. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 52 TF-IDF Weighting TF-IDF weighting : weight(t,d)=TF(t,d)*IDF(t) –Common in doc  high tf  high weight –Rare in collection  high idf  high weight Imagine a word count profile, what kind of terms would have high weights?

53. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 53 How to Measure Similarity? How about Euclidean?

54. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 54 VS Example: Raw TF & Dot Product info retrieval travel map search engine govern president congress IDF(faked) 2.4 4.5 2.8 3.3 2.1 5.4 2.2 3.2 4.3 doc12(4.8) 1(4.5) 1(2.1) 1(5.4) doc21(2.4 ) 2 (5.6) 1(3.3) doc31 (2.2) 1(3.2) 1(4.3) query1(2.4)1(4.5) doc3 information retrieval search engine information travel information map travel government president congress doc1 doc2 …… query=“information retrieval” Sim(q,doc1)=4.8*2.4+4.5*4.5 Sim(q,doc2)=2.4*2.4 Sim(q,doc3)=0

55. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 55 What Works the Best? (Singhal 2001) Use single words Use stat. phrases Remove stop words Stemming Others(?) Error [ ]

56. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 56 Relevance Feedback in VS Basic setting: Learn from examples –Positive examples: docs known to be relevant –Negative examples: docs known to be non-relevant –How do you learn from this to improve performance? General method: Query modification –Adding new (weighted) terms –Adjusting weights of old terms –Doing both The most well-known and effective approach is Rocchio [Rocchio 1971]

57. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 57 + Rocchio Feedback: Illustration q q + + + + ++ + + ++ + + + + + - - - - - - - - - - - - - - - - - - - - - - - - - - - +++

58. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 58 Rocchio Feedback: Formula Origial query Rel docs Non-rel docs Parameters New query

59. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 59 Rocchio in Practice Negative (non-relevant) examples are not very important (why?) Often project the vector onto a lower dimension (i.e., consider only a small number of words that have high weights in the centroid vector) (efficiency concern) Avoid “training bias” (keep relatively high weight on the original query weights) (why?) Can be used for relevance feedback and pseudo feedback Usually robust and effective

60. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 60 “Extension” of VS Model Alternative similarity measures –Many other choices (tend not to be very effective) –P-norm (Extended Boolean): matching a Boolean query with a TF-IDF document vector Alternative representation –Many choices (performance varies a lot) –Latent Semantic Indexing (LSI) [TREC performance tends to be average] Generalized vector space model –Theoretically interesting, not seriously evaluated

61. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 61 Advantages of VS Model Empirically effective! (Top TREC performance) Intuitive Easy to implement Well-studied/Most evaluated The Smart system –Developed at Cornell: 1960-1999 –Still widely used Warning: Many variants of TF-IDF!

62. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 62 Disadvantages of VS Model Assume term independence Assume query and document to be the same Lack of “predictive adequacy” –Arbitrary term weighting –Arbitrary similarity measure Lots of parameter tuning!

63. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 63 What You Should Know What is Vector Space Model (a family of models) What is TF-IDF weighting What is pivoted normalization weighting How Rocchio works

64. 2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 64 Roadmap This lecture –Basic concepts of TR –Evaluation –Common components –Vector space model Next lecture: continue overview of IR –IR system implementation –Other retrieval models –Applications of basic TR techniques