Information Retrieval

Information Retrieval

CSE 8337 (Part IV)Spring 2011

Some Material for these slides obtained from:Modern Information Retrieval by Ricardo Baeza-Yates and Berthier Ribeiro-Neto

http://www.sims.berkeley.edu/~hearst/irbook/Data Mining Introductory and Advanced Topics by Margaret H. Dunham

http://www.engr.smu.edu/~mhd/bookIntroduction to Information Retrieval by Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schutze

http://informationretrieval.org

http://www.sims.berkeley.edu/~hearst/irbook/

http://www.engr.smu.edu/~mhd/book

http://informationretrieval.org/

CSE 8337 Spring 2011 2

CSE 8337 Outline• Introduction• Text Processing• Indexes• Boolean Queries• Web Searching/Crawling• Vector Space Model• Matching• Evaluation• Feedback/Expansion


Why System Evaluation? There are many retrieval models/

algorithms/ systems, which one is the best?

What does best mean? IR evaluation may not actually look at

traditional CS metrics of space/time. What is the best component for:

Ranking function (dot-product, cosine, …) Term selection (stopword removal,

stemming…) Term weighting (TF, TF-IDF,…)

How far down the ranked list will a user need to look to find some/all relevant documents?


Measures for a search engine How fast does it index

Number of documents/hour (Average document size)

How fast does it search Latency as a function of index size

Expressiveness of query language Ability to express complex information

needs Speed on complex queries

Uncluttered UI Is it free?


Measures for a search engine All of the preceding criteria are

measurable: we can quantify speed/size; we can make expressiveness precise

The key measure: user happiness What is this? Speed of response/size of index are factors But blindingly fast, useless answers won’t

make a user happy Need a way of quantifying user

happiness


Happiness: elusive to measure Most common proxy: relevance of

search results But how do you measure relevance? We will detail a methodology here, then

examine its issues Relevant measurement requires 3

elements:1. A benchmark document collection2. A benchmark suite of queries3. A usually binary assessment of either

Relevant or Nonrelevant for each query and each document


Difficulties in Evaluating IR Systems

Effectiveness is related to the relevancy of retrieved items.

Relevancy is not typically binary but continuous.

Even if relevancy is binary, it can be a difficult judgment to make.

Relevancy, from a human standpoint, is: Subjective: Depends upon a specific user’s

judgment. Situational: Relates to user’s current needs. Cognitive: Depends on human perception and

behavior. Dynamic: Changes over time.


How to perform evaluation

Start with a corpus of documents. Collect a set of queries for this corpus. Have one or more human experts

exhaustively label the relevant documents for each query.

Typically assumes binary relevance judgments.

Requires considerable human effort for large document/query corpora.


IR Evaluation Metrics Precision/Recall

P/R graph Regular Smoothing

Interpolating Averaging

ROC Curve MAP R-Precision P/R points

F-Measure E-Measure Fallout Novelty Coverage Utility ….

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documents relevant of number Totalretrieved documents relevant of Number recall

retrieved documents of number Totalretrieved documents relevant of Number precision

Relevant documents

Retrieved documents

Entire document collection

retrieved & relevant

not retrieved but relevant

retrieved & irrelevant

Not retrieved & irrelevant

retrieved not retrieved

rele

vant

irrel

evan

t

Precision and Recall

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Determining Recall is Difficult Total number of relevant items is

sometimes not available: Sample across the database and

perform relevance judgment on these items.

Apply different retrieval algorithms to the same database for the same query. The aggregate of relevant items is taken as the total relevant set.

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Trade-off between Recall and Precision

10

1

Recall

Prec

isio

n

The ideal

Desired areas

Returns relevant documents butmisses many useful ones too

Returns most relevantdocuments but includes lots of junk

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Recall-Precision Graph Example

Recall-Precision Graph

00.20.40.60.8

1

0 0.5 1Recall

Prec

ision

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A precision-recall curve

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.2 0.4 0.6 0.8 1.0

Pre

cisi

on

Recall

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Recall-Precision Graph Smoothing Avoid sawtooth lines by smoothing Interpolate for one query Average across queries

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Interpolating a Recall/Precision Curve

Interpolate a precision value for each standard recall level: rj {0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9,

1.0} r0 = 0.0, r1 = 0.1, …, r10=1.0

The interpolated precision at the j-th standard recall level is the maximum known precision at any recall level between the j-th and (j + 1)-th level: )(max)(

1

rPrPjj rrrj

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Interpolated precision Idea: If locally precision increases with

increasing recall, then you should get to count that…

So you max of precisions to right of value(Need not be at only standard levels.)

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Precision across queries Recall and Precision are calculated for a

specific query. Generally want a value for many

queries. Calculate average precision recall over

a set of queries. Average precision at recall level r:

Nq – number of queries Pi(r) - precision at recall level r for ith

query

1

( )( )qN

i

i q

P rP rN

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Average Recall/Precision Curve

Typically average performance over a large set of queries.

Compute average precision at each standard recall level across all queries.

Plot average precision/recall curves to evaluate overall system performance on a document/query corpus.

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Compare Two or More Systems The curve closest to the upper right-

hand corner of the graph indicates the best performance

00.2

0.40.6

0.81

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1Recall

Prec

ision

NoStem Stem

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Operating Characteristic Curve (ROC Curve)

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ROC Curve Data False Positive Rate vs True Positive

Rate True Positive Rate

Sensitivity – proportion of positive results received

Recall tn/(fp+tn)

False Positive Rate fp/(fp+tn) 1-Specificity Specificity – proportion of negative

results not received

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Yet more evaluation measures…

Mean average precision (MAP) Average of the precision value obtained for

the top k documents, each time a relevant doc is retrieved

Avoids interpolation, use of fixed recall levels

MAP for query collection is arithmetic ave. Macro-averaging: each query counts equally

R-precision If have known (though perhaps incomplete)

set of relevant documents of size Rel, then calculate precision of top Rel docs returned

Perfect system could score 1.0.

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Variance For a test collection, it is usual that a

system does poor on some information needs (e.g., MAP = 0.1) and well on others (e.g., MAP = 0.7)

Indeed, it is usually the case that the variance in performance of the same system across queries is much greater than the variance of different systems on the same query.

That is, there are easy information needs and hard ones!

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Evaluation Graphs are good, but people want summary measures!

Precision at fixed retrieval level Precision-at-k: Precision of top k results Perhaps appropriate for most of web search: all

people want are good matches on the first one or two results pages

But: averages badly and has an arbitrary parameter of k

11-point interpolated average precision The standard measure in the early TREC

competitions: you take the precision at 11 levels of recall varying from 0 to 1 by tenths of the documents, using interpolation (the value for 0 is always interpolated!), and average them

Evaluates performance at all recall levels

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Typical (good) 11 point precisions

SabIR/Cornell 8A1 11pt precision from TREC 8 (1999)

0

0.2

0.4

0.6

0.8

1

0 0.2 0.4 0.6 0.8 1

Recall

Prec

isio

n

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Computing Recall/Precision Points

For a given query, produce the ranked list of retrievals.

Adjusting a threshold on this ranked list produces different sets of retrieved documents, and therefore different recall/precision measures.

Mark each document in the ranked list that is relevant according to the gold standard.

Compute a recall/precision pair for each position in the ranked list that contains a relevant document.

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R=3/6=0.5; P=3/4=0.75

Computing Recall/Precision Points: An Example (modified from [Salton83])

n doc # relevant1 588 x2 589 x3 5764 590 x5 9866 592 x7 9848 9889 57810 98511 10312 59113 772 x14 990

Let total # of relevant docs = 6Check each new recall point:

R=1/6=0.167; P=1/1=1

R=2/6=0.333; P=2/2=1

R=5/6=0.833; p=5/13=0.38

R=4/6=0.667; P=4/6=0.667

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F-Measure One measure of performance that takes

into account both recall and precision. Harmonic mean of recall and precision:

Calculated at a specific document in the ranking.

Compared to arithmetic mean, both need to be high for harmonic mean to be high.

Compromise between precision and recall

PRRPPRF 11

22

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A combined measure: F Combined measure that assesses

precision/recall tradeoff is F measure (weighted harmonic mean):

People usually use balanced F1 measure i.e., with = 1 or = ½

RPPR

RP

F

2

2 )1(1)1(1

1

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E Measure (parameterized F Measure)

A variant of F measure that allows weighting emphasis on precision or recall:

Value of controls trade-off: = 1: Equally weight precision and recall

(E=F). > 1: Weight precision more. < 1: Weight recall more.

PRRPPRE

1

2

2

2

2

)1()1(

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Fallout Rate Problems with both precision and

recall: Number of irrelevant documents in

the collection is not taken into account.

Recall is undefined when there is no relevant document in the collection.

Precision is undefined when no document is retrieved.

collection the in items tnonrelevan of no. totalretrieved items tnonrelevan of no. Fallout

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Fallout Want fallout to be close to 0. In general want to maximize recall

and minimize fallout. Examine fallout-recall graph. More

systems oriented than recall-precision.

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Subjective Relevance Measures Novelty Ratio: The proportion of items

retrieved and judged relevant by the user and of which they were previously unaware. Ability to find new information on a topic.

Coverage Ratio: The proportion of relevant items retrieved out of the total relevant documents known to a user prior to the search. Relevant when the user wants to locate

documents which they have seen before (e.g., the budget report for Year 2000).

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Utility Subjective measure Cost-Benefit Analysis for retrieved

documents Cr – Benefit of retrieving relevant document Cnr – Cost of retrieving a nonrelevant

document Crn – Cost of not retrieving a relevant

document Nr – Number of relevant documents retrieved Nnr – Number of nonrelevant documents

retrieved Nrn – Number of relevant documents not

retrieved

Nrn))* (Crn Nnr)*((Cnr - Nr) (Cr Utility

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Other Factors to Consider User effort: Work required from the user

in formulating queries, conducting the search, and screening the output.

Response time: Time interval between receipt of a user query and the presentation of system responses.

Form of presentation: Influence of search output format on the user’s ability to utilize the retrieved materials.

Collection coverage: Extent to which any/all relevant items are included in the document corpus.

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Experimental Setup for Benchmarking

Analytical performance evaluation is difficult for document retrieval systems because many characteristics such as relevance, distribution of words, etc., are difficult to describe with mathematical precision.

Performance is measured by benchmarking. That is, the retrieval effectiveness of a system is evaluated on a given set of documents, queries, and relevance judgments.

Performance data is valid only for the environment under which the system is evaluated.

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Benchmarks A benchmark collection contains:

A set of standard documents and queries/topics.

A list of relevant documents for each query.

Standard collections for traditional IR:TREC: http://trec.nist.gov/

Standard document collection

Standard queries

Algorithm under test Evaluation

Standard result

Retrieved result

Precision and recall

http://trec.nist.gov/

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Benchmarking The Problems

Performance data is valid only for a particular benchmark.

Building a benchmark corpus is a difficult task.

Benchmark web corpora are just starting to be developed.

Benchmark foreign-language corpora are just starting to be developed.

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The TREC Benchmark • TREC: Text REtrieval Conference (http://trec.nist.gov/) Originated from the TIPSTER program sponsored by Defense Advanced Research Projects Agency (DARPA).• Became an annual conference in 1992, co-sponsored

by the National Institute of Standards and Technology (NIST) and DARPA.• Participants are given parts of a standard set of

documents and TOPICS (from which queries have to be derived) in different stages for training and testing.• Participants submit the P/R values for the final

document and query corpus and present their results at the conference.

http://trec.nist.gov/

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The TREC Objectives • Provide a common ground for comparing different

IR techniques.

– Same set of documents and queries, and same evaluation method.

• Sharing of resources and experiences in developing the

benchmark.– With major sponsorship from government to develop

large benchmark collections.• Encourage participation from industry and

academia.• Development of new evaluation techniques,

particularly for new applications.

– Retrieval, routing/filtering, non-English collection, web-based collection, question answering.

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From document collections to test collections

Still need Test queries Relevance assessments

Test queries Must be germane to docs available Best designed by domain experts Random query terms generally not a good

idea Relevance assessments

Human judges, time-consuming Are human panels perfect?

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Kappa measure for inter-judge (dis)agreement Kappa measure

Agreement measure among judges Designed for categorical judgments Corrects for chance agreement

Kappa = [ P(A) – P(E) ] / [ 1 – P(E) ] P(A) – proportion of time judges agree P(E) – what agreement would be by chance Kappa = 0 for chance agreement, 1 for total

agreement.

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Kappa Measure: ExampleNumber of docs Judge 1 Judge 2

300 Relevant Relevant

70 Nonrelevant Nonrelevant

20 Relevant Nonrelevant

10 Nonrelevant relevant

P(A)? P(E)?

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Kappa Example P(A) = 370/400 = 0.925 P(nonrelevant) = (10+20+70+70)/800 = 0.2125 P(relevant) = (10+20+300+300)/800 = 0.7878 P(E) = 0.2125^2 + 0.7878^2 = 0.665 Kappa = (0.925 – 0.665)/(1-0.665) = 0.776

Kappa > 0.8 = good agreement 0.67 < Kappa < 0.8 -> “tentative conclusions”

(Carletta ’96) Depends on purpose of study For >2 judges: average pairwise kappas

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Interjudge Agreement: TREC 3

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Impact of Inter-judge Agreement Impact on absolute performance measure can be

significant (0.32 vs 0.39) Little impact on ranking of different systems or

relative performance Suppose we want to know if algorithm A is better

than algorithm B A standard information retrieval experiment will

give us a reliable answer to this question.

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Critique of pure relevance Relevance vs Marginal Relevance

A document can be redundant even if it is highly relevant

Duplicates The same information from different sources Marginal relevance is a better measure of

utility for the user. Using facts/entities as evaluation units

more directly measures true relevance. But harder to create evaluation set

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Can we avoid human judgment?

No Makes experimental work hard

Especially on a large scale In some very specific settings, can use proxies

E.g.: for approximate vector space retrieval, we can compare the cosine distance closeness of the closest docs to those found by an approximate retrieval algorithm

But once we have test collections, we can reuse them (so long as we don’t overtrain too badly)

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Evaluation at large search engines

Search engines have test collections of queries and hand-ranked results

Recall is difficult to measure on the web Search engines often use precision at top k, e.g., k = 10 . . . or measures that reward you more for getting rank 1

right than for getting rank 10 right. NDCG (Normalized Cumulative Discounted Gain)

Search engines also use non-relevance-based measures. Clickthrough on first result

Not very reliable if you look at a single clickthrough … but pretty reliable in the aggregate.

Studies of user behavior in the lab A/B testing

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A/B testing Purpose: Test a single innovation Prerequisite: You have a large search engine up and

running. Have most users use old system Divert a small proportion of traffic (e.g., 1%) to the

new system that includes the innovation Evaluate with an “automatic” measure like

clickthrough on first result Now we can directly see if the innovation does

improve user happiness. Probably the evaluation methodology that large

search engines trust most In principle less powerful than doing a multivariate

regression analysis, but easier to understand Problems with A/B Testing:

http://www.sigkdd.org/explorations/issues/12-2-2010-12/v12-02-8-UR-Kohavi.pdf




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CSE 8337 Outline• Introduction• Text Processing• Indexes• Boolean Queries• Web Searching/Crawling• Vector Space Model• Matching• Evaluation• Feedback/Expansion

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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.

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How can results be improved? Options for improving result

Local methods Personalization Relevance feedback Pseudo relevance feedback

Query expansion Local Analysis Thesauri Automatic thesaurus generation

Query assist

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Relevance Feedback Relevance feedback: user feedback on

relevance of docs in initial set of results User issues a (short, simple) query The user marks some results as relevant or

non-relevant. The system computes a better representation

of the information need based on feedback. Relevance feedback can go through one or

more iterations. Idea: it may be difficult to formulate a

good query when you don’t know the collection well, so iterate

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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, multi-pass process.

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Relevance feedback We will use ad hoc retrieval to refer to

regular retrieval without relevance feedback.

We now look at four examples of relevance feedback that highlight different aspects.

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Similar pages

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Relevance Feedback: Example Image search engine

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Results for Initial Query

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Relevance Feedback

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Results after Relevance Feedback

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Initial query/results Initial query: New space satellite

applications1. 0.539, 08/13/91, NASA Hasn’t Scrapped Imaging Spectrometer2. 0.533, 07/09/91, NASA Scratches Environment Gear From Satellite

Plan3. 0.528, 04/04/90, Science Panel Backs NASA Satellite Plan, But

Urges Launches of Smaller Probes4. 0.526, 09/09/91, A NASA Satellite Project Accomplishes Incredible

Feat: Staying Within Budget5. 0.525, 07/24/90, Scientist Who Exposed Global Warming Proposes

Satellites for Climate Research6. 0.524, 08/22/90, Report Provides Support for the Critics Of Using

Big Satellites to Study Climate7. 0.516, 04/13/87, Arianespace Receives Satellite Launch Pact From

Telesat Canada8. 0.509, 12/02/87, Telecommunications Tale of Two Companies

User then marks relevant documents with “+”.

++

+

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Expanded query after relevance feedback 2.074 new 15.106 space 30.816 satellite 5.660

application 5.991 nasa 5.196 eos 4.196 launch 3.972 aster 3.516 instrument 3.446 arianespace 3.004 bundespost 2.806 ss 2.790 rocket 2.053 scientist 2.003 broadcast 1.172 earth 0.836 oil 0.646 measure

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Results for expanded query1. 0.513, 07/09/91, NASA Scratches Environment Gear From

Satellite Plan2. 0.500, 08/13/91, NASA Hasn’t Scrapped Imaging Spectrometer3. 0.493, 08/07/89, When the Pentagon Launches a Secret

Satellite, Space Sleuths Do Some Spy Work of Their Own4. 0.493, 07/31/89, NASA Uses ‘Warm’ Superconductors For Fast

Circuit5. 0.492, 12/02/87, Telecommunications Tale of Two Companies6. 0.491, 07/09/91, Soviets May Adapt Parts of SS-20 Missile For

Commercial Use7. 0.490, 07/12/88, Gaping Gap: Pentagon Lags in Race To Match

the Soviets In Rocket Launchers8. 0.490, 06/14/90, Rescue of Satellite By Space Agency To Cost

$90 Million

21

8

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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

nonrelevant documents.

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Relevance Feedback Architecture

RankingsIRSystem

Documentcorpus

RankedDocuments

1. Doc1 2. Doc2 3. Doc3 . .

1. Doc1 2. Doc2 3. Doc3 . .

Feedback

Query String

RevisedQuery

ReRankedDocuments

1. Doc2 2. Doc4 3. Doc5 . .

QueryReformulation

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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.

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Relevance Feedback in vector spaces We can modify the query based on

relevance feedback and apply standard vector space model.

Use only the docs that were marked. Relevance feedback can improve recall

and precision Relevance feedback is most useful for

increasing recall in situations where recall is important Users can be expected to review results and

to take time to iterate

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The Theoretically Best Query

xx

x xo oo

Optimal query

x non-relevant documentso relevant documents

o

o

o

x x

xxx

x

x

x

x

x

x

xx

x

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Query Reformulation for Vectors 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 both adds both positive and

negatively weighted terms to the query as well as reweighting the initial terms.

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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

jrCd

jr

opt dCN

dC

q

11

Where N is the total number of documents.

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Standard Rocchio 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

jnDd

jr

m dD

dD

qq

: Tunable weight for initial query.: Tunable weight for relevant documents.: Tunable weight for irrelevant documents.

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Relevance feedback on initial query

xx

xxo oo

Revised query

x known non-relevant documentso known relevant documents

o

o

ox

x

x x

xx

x

x

xx

x

x x

x

Initial query

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Positive vs Negative Feedback

Positive feedback is more valuable than negative feedback (so, set < ; e.g. = 0.25, = 0.75).

Many systems only allow positive feedback (=0).

Why?

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Ide Regular Method Since more feedback should

perhaps increase the degree of reformulation, do not normalize for amount of feedback:

njrj Dd

jDd

jm ddqq

: Tunable weight for initial query.: Tunable weight for relevant documents.: Tunable weight for irrelevant documents.

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Ide “Dec Hi” Method Bias towards rejecting just the

highest ranked of the irrelevant documents:

)(max jrelevantnonDd

jm ddqqrj

: Tunable weight for initial query.: Tunable weight for relevant documents.: Tunable weight for irrelevant document.

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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.

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Relevance Feedback: Assumptions A1: User has sufficient knowledge for

initial query. A2: Relevance prototypes are “well-

behaved”. Term distribution in relevant documents will

be similar Term distribution in non-relevant documents

will be different from those in relevant documents

Either: All relevant documents are tightly clustered around a single prototype.

Or: There are different prototypes, but they have significant vocabulary overlap.

Similarities between relevant and irrelevant documents are small

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Violation of A1

User does not have sufficient initial knowledge.

Examples: Misspellings (Brittany Speers). Cross-language information retrieval. Mismatch of searcher’s vocabulary vs.

collection vocabulary Cosmonaut/astronaut

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Violation of A2 There are several relevance

prototypes. Examples:

Burma/Myanmar Contradictory government policies Pop stars that worked at Burger King

Often: instances of a general concept

Good editorial content can address problem Report on contradictory government

policies

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Relevance Feedback: Problems Long queries are inefficient for typical IR

engine. Long response times for user. High cost for retrieval system. Partial solution:

Only reweight certain prominent terms Perhaps top 20 by term frequency

Users are often reluctant to provide explicit feedback

It’s often harder to understand why a particular document was retrieved after applying relevance feedback

Why?

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Evaluation of relevance feedback strategies

Use q0 and compute precision and recall graph Use qm and compute precision recall graph

Assess on all documents in the collection Spectacular improvements, but … it’s cheating! Partly due to known relevant documents ranked

higher Must evaluate with respect to documents not seen

by user Use documents in residual collection (set of

documents minus those assessed relevant) Measures usually lower than for original query But a more realistic evaluation Relative performance can be validly compared

Empirically, one round of relevance feedback is often very useful. Two rounds is sometimes marginally useful.

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Evaluation of relevance feedback

Second method – assess only the docs not rated by the user in the first round Could make relevance feedback look worse

than it really is Can still assess relative performance of

algorithms Most satisfactory – use two collections

each with their own relevance assessments q0 and user feedback from first collection qm run on second collection and measured

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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.

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Evaluation: Caveat True evaluation of usefulness must

compare to other methods taking the same amount of time.

Alternative to relevance feedback: User revises and resubmits query.

Users may prefer revision/resubmission to having to judge relevance of documents.

There is no clear evidence that relevance feedback is the “best use” of the user’s time.

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Pseudo relevance feedback Pseudo-relevance feedback automates

the “manual” part of true relevance feedback.

Pseudo-relevance algorithm: Retrieve a ranked list of hits for the user’s

query Assume that the top k documents are

relevant. Do relevance feedback (e.g., Rocchio)

Works very well on average But can go horribly wrong for some

queries. Several iterations can cause query drift. Why?

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PseudoFeedback 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.

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Relevance Feedback on the Web

Some search engines offer a similar/related pages feature (this is a trivial form of relevance feedback)

Google Altavista

But some don’t because it’s hard to explain to average user:

Yahoo Excite initially had true relevance feedback, but

abandoned it due to lack of use.

α/β/γ ??

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Excite Relevance FeedbackSpink et al. 2000 Only about 4% of query sessions from a

user used relevance feedback option Expressed as “More like this” link next to

each result But about 70% of users only looked at

first page of results and didn’t pursue things further So 4% is about 1/8 of people extending

search Relevance feedback improved results

about 2/3 of the time

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Query Expansion

In relevance feedback, users give additional input (relevant/non-relevant) on documents, which is used to reweight terms in the documents

In query expansion, users give additional input (good/bad search term) on words or phrases

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How do we augment the user query?

Manual thesaurus E.g. MedLine: physician, syn: doc, doctor, MD,

medico Can be query rather than just synonyms

Global Analysis: (static; of all documents in collection) Automatically derived thesaurus

(co-occurrence statistics) Refinements based on query log mining

Common on the web Local Analysis: (dynamic)

Analysis of documents in result set

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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.

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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”

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Automatic Local Analysis Expand query with terms found in local

clusters. Dl – set of documents retrieved 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.

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Association Matrixw1 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 sjkd D

c f f

fik : Frequency of term i in document k

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Normalized Association Matrix Frequency based correlation factor

favors more frequent terms. Normalize association scores:

Normalized score is 1 if two stems have the same frequency in all documents.

ijjjii

ijij ccc

cs

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Metric Correlation Matrix Association correlation does not

account for the proximity of terms in documents, just co-occurrence frequencies within documents.

Metric correlations account for term proximity.

iu jvVk Vk vu

ij kkrc

),(1

Vi: Set of all occurrences of term i in any document.r(ku,kv): Distance in words between word occurrences ku and kv

( if ku and kv are occurrences in different documents).

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Normalized Metric Correlation Matrix

Normalize scores to account for term frequencies:

ji

ijij VV

cs

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Query Expansion with Correlation Matrix

For each term i in query, expand query with the n terms, j, with the highest value of cij (sij).

This adds semantically related terms in the “neighborhood” of the query terms.

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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.

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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.

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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

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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,

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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”

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Similarity Thesaurus The similarity thesaurus is based on term to

term relationships rather than on a matrix of co-occurrence.

This relationship are not derived directly from co-occurrence of terms inside documents.

They are obtained by considering that the terms are concepts in a concept space.

In this concept space, each term is indexed by the documents in which it appears.

Terms assume the original role of documents while documents are interpreted as indexing elements

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Similarity Thesaurus The following definitions establish

the proper framework t: number of terms in the collection N: number of documents in the

collection fi,j: frequency of occurrence of the

term ki in the document dj tj: vocabulary of document dj

itfj: inverse term frequency for document dj

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Similarity Thesaurus Inverse term frequency for document

dj

To ki is associated a vector

jj t

titf log

),....,,(k ,2,1,i Niii www

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Similarity Thesaurus where wi,j is a weight associated to

index-document pair[ki,dj]. These weights are computed as follows

N

lj

lil

li

jjij

ji

ji

itff

f

itff

f

w

1

22

,

,

,

,

,

))(max

5.05.0(

))(max

5.05.0(

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Similarity Thesaurus The relationship between two

terms ku and kv is computed as a correlation factor c u,v given by

The global similarity thesaurus is built through the computation of correlation factor cu,v for each pair of indexing terms [ku,kv] in the collection

jd

jv,ju,vuvu, wwkkc

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Similarity Thesaurus This computation is expensive Global similarity thesaurus has to

be computed only once and can be updated incrementally

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Query Expansion based on a Similarity Thesaurus

Query expansion is done in three steps as follows:1 Represent the query in the concept space

used for representation of the index terms2 Based on the global similarity thesaurus,

compute a similarity sim(q,kv) between each term kv correlated to the query terms and the whole query q.

3 Expand the query with the top r ranked terms according to sim(q,kv)

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Query Expansion - step one To the query q is associated a

vector q in the term-concept space given by

where wi,q is a weight associated to the index-query pair[ki,q]

iqk

qi kwi

,q

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Query Expansion - step two Compute a similarity sim(q,kv)

between each term kv and the user query q

where cu,v is the correlation factor

qk

vu,qu,vvu

cwkq)ksim(q,

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Query Expansion - step three Add the top r ranked terms according to

sim(q,kv) to the original query q to form the expanded query q’

To each expansion term kv in the query q’ is assigned a weight wv,q’ given by

The expanded query q’ is then used to retrieve new documents to the user

qkqu,

vq'v,

u

w)ksim(q,w

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Query Expansion Sample Doc1 = D, D, A, B, C, A, B, C Doc2 = E, C, E, A, A, D Doc3 = D, C, B, B, D, A, B, C, A Doc4 = A

c(A,A) = 10.991 c(A,C) = 10.781 c(A,D) = 10.781 ... c(D,E) = 10.398 c(B,E) = 10.396 c(E,E) = 10.224

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Query Expansion Sample Query: q = A E E sim(q,A) = 24.298 sim(q,C) = 23.833 sim(q,D) = 23.833 sim(q,B) = 23.830 sim(q,E) = 23.435

New query: q’ = A C D E E w(A,q')= 6.88 w(C,q')= 6.75 w(D,q')= 6.75 w(E,q')= 6.64

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WordNet A more detailed database of semantic

relationships between English words. Developed by famous cognitive

psychologist George Miller and a team at Princeton University.

About 144,000 English words. Nouns, adjectives, verbs, and adverbs

grouped into about 109,000 synonym sets called synsets.

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WordNet Synset Relationships Antonym: front back Attribute: benevolence good (noun to

adjective) Pertainym: alphabetical alphabet (adjective

to noun) Similar: unquestioning absolute Cause: kill die Entailment: breathe inhale Holonym: chapter text (part-of) Meronym: computer cpu (whole-of) Hyponym: tree plant (specialization) Hypernym: fruit apple (generalization)

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WordNet Query Expansion Add synonyms in the same synset. Add hyponyms to add specialized

terms. Add hypernyms to generalize a

query. Add other related terms to expand

query.

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Statistical Thesaurus Existing human-developed thesauri

are not easily available in all languages.

Human thesuari 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.

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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.

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Query Expansion based on a Statistical Thesaurus

Use the thesaurus class to query expansion.

Compute an average term weight wtc for each thesaurus class C

C

wwtc

C

1iCi,

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wtc can be used to compute a thesaurus class weight wc as

5.0C

wtcWc

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Query Expansion Sample

TC = 0.90 NDC = 2.00 MIDF = 0.2

sim(1,3) = 0.99sim(1,2) = 0.40sim(1,2) = 0.40sim(2,3) = 0.29sim(4,1) = 0.00sim(4,2) = 0.00sim(4,3) = 0.00

Doc1 = D, D, A, B, C, A, B, CDoc2 = E, C, E, A, A, DDoc3 = D, C, B, B, D, A, B, C, ADoc4 = A

C1

D1 D2D3 D4

C2C3 C4

C1,3

0.99

C1,3,2

0.29

C1,3,2,4

0.00

idf A = 0.0idf B = 0.3idf C = 0.12idf D = 0.12idf E = 0.60 q'=A B E E

q= A E E

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Problems with this approach initialization of parameters TC,NDC and MIDF TC depends on the collection Inspection of the cluster hierarchy is almost

always necessary for assisting with the setting of TC.

A high value of TC might yield classes with too few terms

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Complete link algorithm This is document clustering algorithm with

produces small and tight clusters Place each document in a distinct cluster. Compute the similarity between all pairs of

clusters. Determine the pair of clusters [Cu,Cv] with the

highest inter-cluster similarity. Merge the clusters Cu and Cv Verify a stop criterion. If this criterion is not met

then go back to step 2. Return a hierarchy of clusters.

Similarity between two clusters is defined as the minimum of similarities between all pair of inter-cluster documents

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Selecting the terms that compose each class

Given the document cluster hierarchy for the whole collection, the terms that compose each class of the global thesaurus are selected as follows Obtain from the user three parameters

TC: Threshold class NDC: Number of documents in class MIDF: Minimum inverse document frequency

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Use the parameter TC as threshold value for determining the document clusters that will be used to generate thesaurus classes

This threshold has to be surpassed by sim(Cu,Cv) if the documents in the clusters Cu and Cv are to be selected as sources of terms for a thesaurus class

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Use the parameter NDC as a limit on the size of clusters (number of documents) to be considered.

A low value of NDC might restrict the selection to the smaller cluster Cu+v

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Consider the set of document in each document cluster pre-selected above.

Only the lower frequency documents are used as sources of terms for the thesaurus classes

The parameter MIDF defines the minimum value of inverse document frequency for any term which is selected to participate in a thesaurus class

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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.

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Example of manual thesaurus

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Thesaurus-based query expansion

For each term, t, in a query, expand the query with synonyms and related words of t from the thesaurus

feline → feline cat May weight added terms less than original query

terms. Generally increases recall Widely used in many science/engineering fields May significantly decrease precision, particularly

with ambiguous terms. “interest rate” “interest rate fascinate evaluate”

There is a high cost of manually producing a thesaurus

And for updating it for scientific changes

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Automatic Thesaurus Generation Attempt to generate a thesaurus automatically by

analyzing the collection of documents Fundamental notion: similarity between two words Definition 1: Two words are similar if they co-occur

with similar words. Definition 2: Two words are similar if they occur in a

given grammatical relation with the same words. You can harvest, peel, eat, prepare, etc. apples and

pears, so apples and pears must be similar. Co-occurrence based is more robust, grammatical

relations are more accurate.

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Co-occurrence Thesaurus Simplest way to compute one is based on term-

term similarities in C = AAT where A is term-document matrix.

wi,j = (normalized) weight for (ti ,dj)

For each ti, pick terms with high values in C

ti

dj N

M

What does C contain if A is a term-doc incidence (0/1) matrix?

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Automatic Thesaurus GenerationExample

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Automatic Thesaurus GenerationDiscussion Quality of associations is usually a problem. Term ambiguity may introduce irrelevant

statistically correlated terms. “Apple computer” “Apple red fruit computer”

Problems: False positives: Words deemed similar that are

not False negatives: Words deemed dissimilar that

are similar Since terms are highly correlated anyway,

expansion may not retrieve many additional documents.

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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.

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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

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Query assist

Would you expect such a feature to increase the queryvolume at a search engine?

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Query assist Generally done by query log

mining Recommend frequent recent

queries that contain partial string typed by user

A ranking problem! View each prior query as a doc – Rank-order those matching partial string …

Information Retrieval

Documents

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