龙星计划课程 : 信息检索 Overview of Text Retrieval: Part 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, [email protected]

http://www.cs.uiuc.edu/


Outline

• Basic Concepts in TR

• Evaluation of TR

• Common Components of a TR system

• Vector Space Retrieval Model



Basic Concepts in TR



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



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!



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



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



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



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



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.



Formal Formulation of TR• Vocabulary V={w1, w2, …, wN} of language

• Query q = q1,…,qm, where qi V

• Document di = di1,…,dimi, where dij V

• Collection C= {d1, …, dk}

• 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)”



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”)



Document Selection vs. Ranking

++

+ +-- -

- - - -

- - - -

-

- - +- -

Doc Selectionf(d,q)=?

++

++

--+

-+

--

- --

---

Doc Rankingf(d,q)=?

1

0

0.98 d1 +0.95 d2 +0.83 d3 -0.80 d4 +0.76 d5 -0.56 d6 -0.34 d7 -0.21 d8 +0.21 d9 -

R’(q)

R’(q)

True R(q)



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!



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]



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.”



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)

Most IR research has focused on finding a good function f



Evaluation in Information Retrieval



Evaluation Criteria

• Effectiveness/Accuracy

– Precision, Recall

• Efficiency

– Space and time complexity

• Usability

– How useful for real user tasks?



Methodology: Cranfield Tradition

• Laboratory testing of system components

– Precision, Recall

– Comparative testing

• Test collections

– Set of documents

– Set of questions

– Relevance judgments



The Contingency Table

Relevant Retrieved

Irrelevant Retrieved Irrelevant Rejected

Relevant RejectedRelevant

Not relevant

Retrieved Not RetrievedDocAction

Relevant

RetrievedRelevant Recall

Retrieved

RetrievedRelevant Precision



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?



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)



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



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



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 = 4System returns 6 docs

Average Prec = (1/1+2/2+3/5+0)/4

about 5.5 docsin the top 10 docs

are relevant

Precision@10docs



What Query Averaging Hides

0

0.1

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0.3

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0.6

0.7

0.8

0.9

1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Recall

Prec

isio

n

Slide from Doug Oard’s presentation, originally from Ellen Voorhees’ presentation



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!



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



Common Components in a TR System



Typical TR System Architecture

User

querydocs

results

Query Rep

Doc Rep (Index)

ScorerIndexer

Tokenizer

Index

judgmentsFeedback



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



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)



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”?



Relevance Feedback

Updatedquery

Feedback

Judgments:d1 +d2 -d3 +

…dk -...

Query RetrievalEngine

Results:d1 3.5d2 2.4…dk 0.5...

UserDocumentcollection



Pseudo/Blind/Automatic Feedback

Query RetrievalEngine

Results:d1 3.5d2 2.4…dk 0.5...

Judgments:d1 +d2 +d3 +

…dk -...

Documentcollection

Feedback

Updatedquery

top 10



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



Overview of Retrieval ModelsRelevance

(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 spacemodel

(Salton et al., 75)

Prob. distr.model

(Wong & Yao, 89)

…

GenerativeModel

RegressionModel

(Fox 83)

Classicalprob. Model(Robertson &

Sparck Jones, 76)

Docgeneration

Querygeneration

LMapproach

(Ponte & Croft, 98)(Lafferty & Zhai, 01a)

Prob. conceptspace model

(Wong & Yao, 95)

Differentinference system

Inference network model

(Turtle & Croft, 91)

Learn toRank

(Joachims 02)(Burges et al. 05)



Retrieval Models: Vector Space



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)



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 ?



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



VS Model: illustration

Java

Microsoft

Starbucks

D6

D10

D9

D4

D7

D8

D5

D11

D2 ? ?

D1

? ?

D3

? ?

Query



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



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”, …



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



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!



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)



TF Normalization (cont.)

Norm. TF

Raw TF

Which curve is more reasonable? Should normalized-TF be up-bounded?

Normalization interacts with the similarity measure



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



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 docsk -- # docs with term t (doc freq)



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?



How to Measure Similarity?

product)dot normalized(

)()(

),( :Cosine

),( :similarityproduct Dot

absent is term a if 0 ),...,(

),...,(

1

2

1

2

1

1

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jij

N

jqj

N

jijqj

i

N

jijqji

qNq

iNii

ww

ww

DQsim

wwDQsim

wwwQ

wwD

How about Euclidean?

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jijqji wwDQsim

1

2)(),(



VS Example: Raw TF & Dot Product

info retrieval travel map search engine govern president congressIDF(faked) 2.4 4.5 2.8 3.3 2.1 5.4 2.2 3.2 4.3

doc1 2(4.8) 1(4.5) 1(2.1) 1(5.4)doc2 1(2.4 ) 2 (5.6) 1(3.3) doc3 1 (2.2) 1(3.2) 1(4.3)

query 1(2.4) 1(4.5)

doc3

information retrievalsearchengine

information

travelinformation

maptravel

government presidentcongress

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



What Works the Best?

(Singhal 2001)

•Use single words

•Use stat. phrases

•Remove stop words

•Stemming

•Others(?)

Error

[ ]



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]



+

Rocchio Feedback: Illustration

qq+ ++++ +

++++

++

++

+---

----

-- --

------ --

-----

-

-

-+ + +



Rocchio Feedback: Formula

Origial query Rel docs Non-rel docs

ParametersNew query



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



“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



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!



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!



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



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


龙星计划课程 : 信息检索 Overview of Text Retrieval: Part 1

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