: Overview of Text Retrieval: Part 1 ChengXiang

 :  Overview of Text Retrieval: Part 1 ChengXiang

: Overview of Text Retrieval: Part 1 ChengXiang Zhai ( ) 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, [email protected] 2008 ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 1 Outline Basic Concepts in TR Evaluation of TR Common Components of a TR system Vector Space Retrieval Model 2008 ChengXiang Zhai

Dragon Star Lecture at Beijing University, June 21-30, 2008 2 Basic Concepts in TR 2008 ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 3 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

2008 ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 4 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! 2008 ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008

5 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 2008 ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 6 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 2008 ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 7 History of TR on One Slide Birth of TR 1945: V. Bushs article As we may think 1957: H. P. Luhns idea of word counting and matching

Indexing & Evaluation Methodology (1960s) Smart system (G. Saltons group) Cranfield test collection (C. Cleverdons group) Indexing: automatic can be as good as manual (controlled vocabulary) TR Models (1970s & 1980s) Large-scale Evaluation & Applications (1990s-Present) TREC (D. Harman & E. Voorhees, NIST) Web search, PubMed, Boundary with related areas are disappearing 2008 ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 8 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 2008 ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 9 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. 2008 ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 10 Formal Formulation of TR Vocabulary V={w , w , , w } of language Query q = q ,,q where q V Document d = d ,,d where d V Collection C= {d , , d } Set of relevant documents R(q) C 1 1 i 2

N m, i i1 imi, 1 k ij Generally unknown and user-dependent Query is a hint on which doc is in R(q) Task = compute R(q), an approximate R(q) 2008 ChengXiang Zhai

Dragon Star Lecture at Beijing University, June 21-30, 2008 11 Computing R(q) Strategy 1: Document selection R(q)={dC|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) = {dC|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) 2008 ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 12

Document Selection vs. Ranking True R(q) + +- -+- + + --- --- 1 Doc Selection f(d,q)=? 0 + +- + ++ R(q) - -- - - + - - 0.98 d1 + 0.95 d2 + Doc Ranking R(q) 0.83 d3 f(d,q)=? 0.80 d4 +

0.76 d5 0.56 d6 0.34 d7 0.21 d8 + 13 2008 ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 0.21 d9 -2008 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! 2008 ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 14 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 justification: Probability Ranking Principle Theoretical [Robertson 77] 2008 ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008

15 Probability Ranking Principle [Robertson 77] As stated by Cooper If a reference retrieval systems 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. Robertson provides two formal justifications Assumptions: Independent relevance and sequential browsing (not necessarily all hold in reality) 2008 ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 16 According to the PRP, all we need is A relevance measure function f

which satisfies For all q, d1, d2, f(q,d1) > f(q,d2) iff p(Rel|q,d1) >p(Rel|q,d2) ost IR research has focused on finding a good function 2008 ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 17 Evaluation in Information Retrieval 2008 ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 18 Evaluation Criteria Effectiveness/Accuracy

Precision, Recall Efficiency Space and time complexity Usability How useful for real user tasks? 2008 ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 19 Methodology: Cranfield Tradition Laboratory testing of system components Precision, Recall Comparative testing Test collections Set of documents Set of questions Relevance judgments

2008 ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 20 The Contingency Table Doc Action Relevant Retrieved Not Retrieved Relevant Retrieved Relevant Rejected Not relevant Irrelevant Retrieved Irrelevant Rejected

Relevant Retrieved Precision Retrieved Relevant Retrieved Recall Relevant 2008 ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 21 How to measure a ranking? Compute the precision at every recall point Plot a precision-recall (PR) curve precision precision x x

Which is better? x x x x x recall 2008 ChengXiang Zhai x recall Dragon Star Lecture at Beijing University, June 21-30, 2008 22 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 2nd 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)

2008 ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 23 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/log22 + r3/log23 + rn/log2n 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 2008 ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 24 When Theres only 1 Relevant Document Scenarios: known-item search navigational queries Search Length = Rank of the answer:

measures a users effort Mean Reciprocal Rank (MRR): Reciprocal Rank: 1/Rank-of-the-answer Take an average over all the queries 2008 ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 25 Precion-Recall Curve Out of 4728 rel docs, weve got 3212 Recall=3212/4728 [email protected] about 5.5 docs in the top 10 docs are relevant Breakeven Point (prec=recall)

Mean Avg. Precision D1 + (MAP) D2 + Total # rel docs = 4 D3 System returns 6 docs D4 Average Prec = (1/1+2/2+3/5+0)/4 D5 + 2008 ChengXiang Zhai D6 - Dragon Star Lecture at Beijing University, June 21-30, 2008 26 What Query Averaging Hides 1 0.9 0.8 Precision 0.7 0.6

0.5 0.4 0.3 0.2 0.1 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

0.9 1 Recall Slide from Doug Oards presentation, originally from Ellen Voorhees presentation 2008 ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 27 The Pooling Strategy When the test collection is very large, its impossible to completely judge all the documents TRECs 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 systems performance may be under-estimated Reuse the data set with caution! 2008 ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 28 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

2008 ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 29 Common Components in a TR System 2008 ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 30 Typical TR System Architecture docs query Feedback Tokenizer Doc Rep (Index)

Indexer Query Rep Index 2008 ChengXiang Zhai judgments User Scorer results Dragon Star Lecture at Beijing University, June 21-30, 2008 31 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 2008 ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 32 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) 2008 ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 33 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? 2008 ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 34 Relevance Feedback Query Retrieval Engine Updated query Document collection

Feedback 2008 ChengXiang Zhai Results: d1 3.5 d2 2.4 dk 0.5 ... User Judgments: d1 + d2 d3 + dk ... Dragon Star Lecture at Beijing University, June 21-30, 2008 35 Pseudo/Blind/Automatic

Feedback Query Retrieval Engine Updated query Document collection Feedback 2008 ChengXiang Zhai Results: d1 3.5 d2 2.4 dk 0.5 ... Judgments: d1 + d2 +

d3 + dk ... top 10 Dragon Star Lecture at Beijing University, June 21-30, 2008 36 What You Should Know How TR is different from DB retrieval How to compute the major evaluation measure (precision, recall, precision-recall curve, MAP, gMAP, breakeven precision, NDCG, MRR)

What is pooling What is relevance feedback; what is pseudo relevance feedback Why ranking is generally preferred to document selection (justified by PRP) What is tokenization (word segmentation, stemming, stop word removal) 2008 ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 37 Overview of Retrieval Models Relevance (Rep(q), Rep(d)) Rep(Rep(q), Rep(d)) q), Rep(Rep(q), Rep(d)) d)) Similarity

Different rep & similarity P(Rep(q), Rep(d)) r=1|q,d) r {0,1} Probability of Relevance Regression Model (Fox 83) Generative Model P(Rep(q), Rep(d)) d q) or P(Rep(q), Rep(d)) q d) Probabilistic inference Different inference system Query Doc Learn to generation generation Inference

Prob. concept Rank network space model (Joachims 02) Vector space model Prob. distr. Classical LM (Wong & Yao, 95) (Burges et al. 05) model (Turtle & Croft, 91) model prob. Model approach (Salton et al., 75) (Wong & Yao, 89) (Robertson & (Ponte & Croft, 98)

Sparck Jones, 76) (Lafferty & Zhai, 01a) 2008 ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 38 Retrieval Models: Vector Space 2008 ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 39 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) 2008 ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 40 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) = {dC|f(d,q)>}, f(q,d)=(Rep(q), Rep(d)) Key issues How to represent query/document? How to define the similarity measure ? 2008 ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 41

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=(x1,,xN), xi is importance of term i Measure relevance by the distance between the query vector and document vector in the vector space 2008 ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 42 VS Model: illustration Starbucks D9 D11

?? ?? D2 D5 D3 D10 D4 D6 Query D7 D8 Java D1 Microsoft ?? 2008 ChengXiang Zhai

Dragon Star Lecture at Beijing University, June 21-30, 2008 43 What the VS model doesnt 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 2008 ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 44 Whats 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, 2008 ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 45 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 2008 ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 46 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: +0.5*f(t,d)/MaxFreq(d) Okapi/BM25 TF:

f(t,d)/(f(t,d)+k(1-b+b*doclen/avgdoclen)) TF(t,d) = 0.5 TF(t,d) = k Normalization of TF is very important! 2008 ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 47 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 overpenalizing (pivoted normalization)

2008 ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 48 TF Normalization (cont.) Norm. TF Raw TF Which curve is more reasonable? Should normalized-TF be up-bounded? Normalization interacts with the similarity measure 2008 ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 49 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 2008 ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 50 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) 2008 ChengXiang Zhai

Dragon Star Lecture at Beijing University, June 21-30, 2008 51 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? 2008 ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 52 How to Measure Similarity? Di ( w i 1 ,..., w iN )

Q ( wq1 ,..., wqN ) w 0 if a term is absent N Dot product similarity : sim(Q , Di ) wqj w ij j 1 N Cosine : 9 sim(Q , Di ) wqj w ij j 1 N ( wqj ) 2

N j 1 ( wij )2 j 1 ( normalized dot product) How about Euclidean? 2008 ChengXiang Zhai 9 sim(Q, Di ) N 2 ( w w

) qj ij j 1 Dragon Star Lecture at Beijing University, June 21-30, 2008 53 VS Example: Raw TF & Dot Product doc1 information retrieval search engine information Sim(q,doc1)=4.8*2.4+4.5*4.5 query=information retrieval Sim(q,doc2)=2.4*2.4 travel information doc2

doc3 map travel government president congress Sim(q,doc3)=0 info IDF(faked) 2.4 doc1 doc2 doc3 2(4.8) 1(2.4 ) query

1(2.4) retrieval 4.5 travel map search engine govern president congress 2.8 3.3 2.1 5.4 2.2 3.2 4.3 1(4.5) 1(2.1) 2 (5.6) 1(3.3) 1(5.4) 1 (2.2) 1(3.2) 1(4.3)

1(4.5) 2008 ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 54 What Works the Best? Error [ ] Use single words Use stat. phrases Remove stop words Stemming Others(?) (Singhal 2001) 2008 ChengXiang Zhai

Dragon Star Lecture at Beijing University, June 21-30, 2008 55 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]

2008 ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 56 Rocchio Feedback: Illustration --- --+ + + + q q -- + - +++ + + - - - + +++ + ++ -- -- -- 2008 ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008

57 Rocchio Feedback: Formula Parameters New query Origial query Rel docs 2008 ChengXiang Zhai Non-rel docs Dragon Star Lecture at Beijing University, June 21-30, 2008 58 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 2008 ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 59

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 2008 ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 60 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! 2008 ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 61 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!

2008 ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 62 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 2008 ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 63 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 2008 ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 2008 64

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