Hsin-Hsi Chen 5-1

Chapter 5Query Operations

Hsin-Hsi Chen

Department of Computer Science and Information Engineering

National Taiwan University

Hsin-Hsi Chen 5-2

Paraphrase Problem in IR

• Users often input queries containing terms that do not match the terms used to index the majority of the relevant documents.

• relevance feedback and query modification– reweighting of the query terms based on the distributio

n of these terms in the relevant and nonrelevant documents retrieved in response to those queries

– changing the actual terms in the query

Hsin-Hsi Chen 5-3

Query Reformulation

• basic steps– query expansion: expanding the original query with ne

w terms• feedback information from the user

• information derived from the set of documents initially retrieved (local set of documents)

• global information derived from document collection

– term reweighting• reweighting the terms in the expanded query

Hsin-Hsi Chen 5-4

User Relevance Feedback

• U: Query is submitted• S: A list of the retrieved documents is presented• U: The documents are examined and the relevant

ones are marked• S: The important terms/expressions are selected

from the documents that have been identified as relevant

• The relevance feedback cycle is repeated several times

Hsin-Hsi Chen 5-5

User Relevance Feedback (Continued)

• advantages– Shield the details of the query reformulation– Break down the whole searching task into a

sequence of small steps– Provide a controlled process designed to

emphasize some terms (relevant ones) and de-emphasize others (non-relevant ones)

Hsin-Hsi Chen 5-6

Query Expansion and Term Reweighting for the Vector Model

• basic idea– Relevant documents resemble each other– Non-relevant documents have term-weight

vectors which are dissimilar from the ones for the relevant documents

– The reformulated query is moved to closer to the term-weight vector space of relevant documents

Hsin-Hsi Chen 5-7

Hsin-Hsi Chen 5-8

Query Expansion and Term Reweighting for the Vector Model (Continued)

Cr: set of relevant documents

set of non-relevant documents

Dr: set of relevant documents, as identified by the userDn: set of non-relevant documents

the retrieveddocuments collection

Hsin-Hsi Chen 5-9

Query Expansion and Term Reweighting for the Vector Model (Continued)

• when complete set Cr of relevant documents is known

• when the set Cr are not known a priori – Formulate an initial query

– Incrementally change the initial query vector

rj rjCd Cd

j

r

j

ropt d

CNd

Cq

||

1

||

1

Hsin-Hsi Chen 5-10

• Calculate the modified query– Standard-Rochio

– Ide-Regular

– Ide-Dec-Hi

, , : tuning constants (usually, >) =1 (Rochio, 1971) ===1 (Ide, 1971) =0: positive feedback

Dnd

jnDrd

jr

mjj

dD

dD

qq||||

Dnd

jDrd

jmjj

ddqq

)(max jrelevantnonDrd

jm ddqqj

the highest ranked non-relevant document

queryexpansion

term reweighting

Similar performance

Hsin-Hsi Chen 5-11

positive relevance-feedback ==1 and =0

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• “dec hi” method: use all relevant information, but subtract only the highest ranked nonrelevant document

• feedback with query splittingsolve problems: (1) the relevant documents identified do not form a tight cluster; (2) nonrelevant documents are scattered among certain relevant ones

homogeneousrelevant items

homogeneousrelevant items

Hsin-Hsi Chen 5-13

Analysis

• advantages– simplicity– good results

• disadvantages– No optimality criterion is adopted

Hsin-Hsi Chen 5-14

Term Weighting for the Probabilistic Model

• The similarity of a document dj to a query q

))|(

))|(1(log

))|(1(

)|((log),(

1,, RkP

RkP

RkP

RkPwwqdsim

i

i

i

it

ijiqij

)|( RkP i : the probability of observing the term ki in the set R of relevant documents

)|( RkP i : the probability of observing the term ki in the set R of non-relevant documents

5.0)|( RkP i

Initial search:

N

nRkP i

i )|(

Hsin-Hsi Chen 5-15

i

it

ijiqi

i

it

ijiqi

i

i

i

it

ijiqij

n

nNww

Nn

Nn

ww

RkP

RkP

RkP

RkPwwqdsim

log

))1(

log)5.01(

5.0(log

))|(

))|(1(log

))|(1(

)|((log),(

1,,

1,,

1,,

Feedback search:

5.0)|( RkP iInitial search:N

nRkP i

i )|(

||

||)|( ,

r

irii D

D

V

VRkP

||

||)|( ,

r

iriiii DN

Dn

VN

VnRkP

Hsin-Hsi Chen 5-16

Feedback search:

||

||)|( ,

r

iri D

DRkP

||

||)|( ,

r

irii DN

DnRkP

||

|)|(||

||||

||log

||

||||

||1

||

||1

||

||

log

))|(

))|(1(log

))|(1(

)|((log),(

,

,

,

,

1,,

,

,

,

,

1,,

1,,

iri

irir

irr

irt

ijiqi

r

iri

r

iri

r

ir

r

irt

ijiqi

i

i

i

it

ijiqij

Dn

DnDN

DD

Dww

DN

DnDN

Dn

D

DD

D

ww

RkP

RkP

RkP

RkPwwqdsim

No query expansion occurs

Hsin-Hsi Chen 5-17

1||

5.0||

1

5.0)|(

1||

5.0||

1

5.0)|(

,

,

r

iriiii

r

irii

DN

Dn

VN

VnRkP

D

D

V

VRkP

For small values of |Dr| and |Dr,i| (i.e., |Dr|=1, |Dr,i|=0)

Alternative 1:

Alternative 2:

1||

||)|(

1||

||)|(

,

,

r

iiri

i

r

iir

i

DNNn

DnRkP

DNn

DRkP

Hsin-Hsi Chen 5-18

Analysis

• advantages– Feedback process is directly related to the derivation of

new weights for query terms

– The term reweighting is optimal

• disadvantages– Document term weights are not considered

– Weights of terms in previous query formulations are disregarded

– No query expansion is used

Hsin-Hsi Chen 5-19

A Variant of Probabilistic Term Reweighting

• variant– distinct initial search method– include within-document frequency weights

• initial search

)max()1(

)(

),(

,

,,

,,,

1,,,,

ji

jiji

jiiqji

t

iqjijiqij

f

fKKf

fidfCF

Fwwqdsim

Similar totf-idf scheme

Hsin-Hsi Chen 5-20

C=0 for automatically indexed collections or for feedback searching (allow IDF or the relevance weighting to be the dominant factor)C>0 for manually indexed collections (allow the mere existence of a term within a document to carry more weight)

K=0.3 for initial search of regular length documents (documents having many multiple occurrences of a term)K=0.5 for feedback searchesK=1 for short documents: the within-document frequency is removed (the within-document frequency plays a minimum role)

Feedback search

jii

i

i

iqji f

RkP

RkP

RkP

RkPCF ,,, )

)|(

)|(1log

)|(1

)|(log(

Hsin-Hsi Chen 5-21

Analysis

• advantages– The within-document frequencies are considered– A normalized version of these frequencies is

adopted– Constants C and K are introduced

• disadvantages– more complex formulation– no query expansion

Hsin-Hsi Chen 5-22

Evaluation of relevance feedback• Standard evaluation (i.e., recall-precision) method

is not suitable, because the relevant documents used to reweight the query terms moving to higher ranks.

• The residual collection method– the evaluation of the results compares only the residual

collections, i.e., the initial run is remade minus the documents previously shown to the user and this is compared with the feedback run minus the same documents

Note that qm tend to be lowerthan the figures for the originalquery vector q in residual collection

Hsin-Hsi Chen 5-23

Residual Collection with Partial Rank Freezing

• The previously retrieved items identified as relevant are kept “frozen”; and the previously retrieved nonrelevant items are simple removed from the collection.

Assume 10 documents are relevant.

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Residual Collection with Partial Rank Freezing

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Automatic Local Analysis

• user relevance feedback– Known relevant documents contain terms which can be

used to describe a larger cluster of relevant documents with assistance from the user (clustering)

• automatic analysis– Obtain a description (i.t.o terms) for a larger cluster of

relevant documents automatically– global strategy: global thesaurus-like structure is trained

from all documents before querying – local strategy: terms from the documents retrieved for a

given query are selected at query time

Hsin-Hsi Chen 5-26

Local Feedback Strategy

• Internet– client site

• Retrieving the text of 100 Web documents for local analysis would take too long

– server site• Analyzing the text of 100 Web documents would spend extra

CPU time

• Applications– Intranet– Specialized document collections, e.g., medical

document collections

Hsin-Hsi Chen 5-27

Query Expansion-Local Clustering

• stem– V(s): a non-empty subset of words which are grammatical v

ariants of each othere.g., {polish, polishing, polished}

– A canonical form s of V(s) is called a steme.g., polish

• local document set Dl

– the set of documents retrieved for a given query

• local vocabulary Vl (Sl)– the set of all distinct words (stems) in the local document se

t

Hsin-Hsi Chen 5-28

local cluster

• basic concept– Expanding the query with terms correlated to the query terms

– The correlated terms are presented in the local clusters built from the local document set

• local clusters– association clusters: co-occurrences of pairs of terms in docu

ments

– metric clusters: distance factor between two terms

– scalar clusters: terms with similar neighborhoods have some synonymity relationship

Hsin-Hsi Chen 5-29

Association Clusters

• idea– Based on the co-occurrence of stems (or terms) insi

de documents

• association matrix– fsi,j: the frequency of a stem si in a document dj (Dl)

– m=(fsi,j): an association matrix with |Sl| rows and |Dl| columns

– : a local stem-stem association matrixt

mms

Hsin-Hsi Chen 5-30

jsv

Dldj

jsuvu ffc ,,,

vuvvuu

vuvu ccc

cs

,,,

,,

: a correlation between the stems su and sv

: normalized matrix

su,v=cu,v: unnormalized matrix

:)(nsu local association cluster around the stem su

Take u-th rowReturn the set of n largest values su,v (uv)

an element in t

mm

Hsin-Hsi Chen 5-31

Metric Clusters

• idea– Consider the distance between two terms in th

e computation of their correlation factor

• local stem-stem metric correlation matrix– r(ki,kj): the number of words between keywords

ki and kj in a same document

– cu,v: metric correlation between stems su and sv

)()(

, ),(

1

svVkj jisuVki

vu kkrc

),(1

),(ji

ji kkrkkr

Hsin-Hsi Chen 5-32

|)(||)(|,

,vu

vuvu sVsV

cs

: normalized matrix

su,v=cu,v: unnormalized matrix

:)(nsu local metric cluster around the stem su

Take u-th rowReturn the set of n largest values su,v (uv)

Hsin-Hsi Chen 5-33

Scalar Clusters

• idea– Two stems with similar neighborhoods have s

ynonymity relationship– The relationship is indirect or induced by the n

eighborhood

• scalar association matrix ||||,

vu

vuvu

ss

sss

:)(nsu local scalar cluster around the stem su

Take u-th rowReturn the set of n largest values su,v (uv)

The row corresponding to a specific termin a term co-occurrence matrix forms its neighborhood

The correlation value for su andsv in this matrix may be small

Hsin-Hsi Chen 5-34

Interactive Search Formulation

• neighbors of the query term sv

– Terms su belonging to clusters associated to sv, i.e., suSv(n)

– su is called a searchonym of sv x

Su

Sv

Sv(n)

x

xx

x

x

x

x

x

x

x

x

x

x

xx x

x

x

x

Hsin-Hsi Chen 5-35

Interactive Search Formulation(Continued)

• Algorithm– For each stem svq, select m neighbor stems from t

he cluster Sv(n) and add them to the query– Merge normalized and unnormalized clusters

• Extension– Let su and sv be correlated with a cu,v

– If cu,v is larger than a predefined threshold, then a neighbor stem su’ of su can also be interpreted as a neighbor stem of sv, and vice versa.

more rare large frequencies

Hsin-Hsi Chen 5-36

Query Expansion throughLocal Context Analysis

• local analysis– Based on the set of documents retrieved for the

original query– Based on term co-occurrence inside documents– Terms closest to individual query terms are selected

• global analysis– Based on the whole document collection– Based on term co-occurrence inside small contexts

and phrase structures– Terms closest to the whole query are selected

Hsin-Hsi Chen 5-37

Query Expansion throughLocal Context Analysis (Continued)

• candidates– noun groups instead of simple keywords– single noun, two adjacent nouns, or three

adjacent nouns

• query expansion– Concepts are selected from the top ranked

documents (as in local analysis)– Passages are used for determining co-

occurrence (as in global analysis)

Hsin-Hsi Chen 5-38

Query Expansion throughLocal Context Analysis (Continued)

• algorithm– Retrieve the top n ranked passages using the original q

uery– For each concept in the top ranked passages, the similar

ity sim(q,c) between the whole query q and the concept c is computed using a variant of tf-idf ranking

– The top m ranked concepts are added to the original query q

• Each concept is assigned a weight 1-0.9i/m (i: rank)• Each term in the original query is assigned a weight 2origina

l weight

Hsin-Hsi Chen 5-39

i

i

idf

qk

cin

idfkcfcqsim

log

,log),(

jcn

jjii pfpfkcf ,

1,,

5

log,1max 10 i

inpN

idf

5

log,1max 10 cnpN

idfc

n: # of rankedpassages

correlation between c and ki

pfi,j (pfc,j): freq of ki (c) in j-th passage

N: # of passages in the collectionnpi: # of passages containing term ki

npc: # of passages containing concept c

0.1

idf1，當 np很大 (小 )時，第二項可能小 (大 )於 1

association clusters (passage)

for infrequent query term

Hsin-Hsi Chen 5-40

Automatic Global Analysis

• local analysis– Extract information from the local set of documents

(passages) retrieved

• global analysis– Expand the query using information from the whole set

of documents in the collection– Issues

• How to build the thesaurus• How to select the terms for query expansion

– Query expansion based on similarity thesaurus– Query expansion based on statistical thesaurus

Hsin-Hsi Chen 5-41

Similarity Thesaurus

• How to build the thesaurus– Consider term to term relationship instead of

co-occurrence

• How to select the terms for query expansion– Consider the similarity to the whole query

instead of individual query terms

Hsin-Hsi Chen 5-42

Concept Space

• basic idea– Each term is indexed by the documents in which it appears– The role of terms and documents is interchanged in the co

ncept space

• t: the number of terms in the collection• N: the number of documents in the collection• fi,j: the frequency of term ki in document dj

• tj: the number of distinct index terms in document dj

• itfj: inverse term frequency for document dj

jj t

titf log

(dj 用來區辨 index term的能力，dj含有的 index terms 越多，區辨力越低 )

Hsin-Hsi Chen 5-43

Each term ki is associated with a vector ki

Niiii wwwk ,2,1, ,...,,r

where

Nl jlifl

f ji

jjifj

f ji

itf

itf

jiw

12

2

),(max,

),(max,

5.05.0

5.05.0

,

The relationship between two terms ku and kv is computed as

jd

jvjuvuvu wwkkc ,,,

Hsin-Hsi Chen 5-44

Query Expansion using Global Similarity Thesaurus

• Represent the query in the concept space used for representation of the index terms

• 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

qk

iqii

kwq

,

Qk

vuquvvu

cwkqkqsim ,,,

query term expand term

Hsin-Hsi Chen 5-45

Query Expansion using Global Similarity Thesaurus

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

quk qu

vwkqsim

qvw,

,,

Hsin-Hsi Chen 5-46

Ki

Kv

Kj

Ka Qc Kb

Q={Ka,Kb}

Expand term

Hsin-Hsi Chen 5-47

GVSM vs. Query Expansion

Qk

vuquvvu

cwkqkqsim ,,,

idk jij kwdji

,

vuqudk qk

jvj cwwdqsimjv u

,,,),(

Only the top r ranked terms are used for query expansion.