query operations
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Query Operations
J. H. WangMar. 26, 2008
The Retrieval ProcessUserInterface
Text Operations
Query Operations
Indexing
Searching
Ranking
Index
Text
query
user need
user feedback
ranked docs
retrieved docs
logical viewlogical view
inverted file
DB Manager Module
4, 10
6, 7
5 8
2
8
Text Database
Text
Query Modification
• Improving initial query formulation– Relevance feedback
• approaches based on feedback information from users
– Local analysis • approaches based on information derived
from the set of documents initially retrieved (called the local set of documents)
– Global analysis• approaches based on global information
derived from the document collection
Relevance Feedback
• Relevance feedback process– shields the user from the details of the query
reformulation process– breaks down the whole searching task into a
sequence of small steps which are easier to grasp– provides a controlled process designed to emphasize
some terms and de-emphasize others
• Two basic techniques– Query expansion
• addition of new terms from relevant documents– Term reweighting
• modification of term weights based on the user relevance judgement
Vector Space Model
• Definitionwi,j: the ith term in the vector for document dj
wi,k: the ith term in the vector for query qk
t: the number of unique terms in the data set
t
i
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t
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Query Expansion and Term Reweighting for the Vector
Model• Ideal situation
– CR: set of relevant documents among all documents in the collection
• Rocchio (1965, 1971)– R: set of relevant documents, as identified by
the user among the retrieved documents– S: set of non-relevant documents among the
retrieved documents
RjRj Cdj
RCd
j
Ropt d
CNd
Cq
||
1
||
1
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Rdjm jj
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Rocchio’s Algorithm
• Ide_Regular (1971)
• Ide_Dec_Hi
• Parameters = = =1 >
}|{ SddMaxdqq jjRd
jm j
Sdj
Rdjm jj
ddqq
Probabilistic Model
• Definition– pi: the probability of observing term ti in the set of
relevant documents– qi: the probability of observing term ti in the set of
nonrelevant documents
• Initial search assumption– pi is constant for all terms ti (typically 0.5)– qi can be approximated by the distribution of ti in the
whole collection
t
i ii
iiqijij pq
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iii
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iii idf
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Term Reweighting for the Probabilistic Model
• Robertson and Sparck Jones (1976)• With relevance feedback from user
N: the number of documents in the collectionR: the number of relevant documents for query qni: the number of documents having term ti
ri: the number of relevant documents having term ti
Document Relevance
DocumentIndexing
+
-
+
ri
R-ri
R
N-ni-R+ri
-
ni-ri
N-R
ni
N-ni
N
• Initial search assumption•pi is constant for all terms ti (typically 0.5)•qi can be approximated by the distribution of ti in the whole collection
•With relevance feedback from users•pi and qi can be approximated by
•hence the term weight is updated by
)(R
rp i
i )(RN
rnq ii
i
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iqijij n
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1,, log),(
t
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)(log),(
Term Reweighting for the Probabilistic Model (cont.)
• However, the last formula poses problems for certain small values of R and ri (R=1, ri=0)
• Instead of 0.5, alternative adjustments have been proposed
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Term Reweighting for the Probabilistic Model (Cont.)
• Characteristics– Advantage
• the term reweighting is optimal under the assumptions of
– term independence
– binary document indexing (wi,q {0,1} and wi,j {0,1})
– Disadvantage• no query expansion is used• weights of terms in the previous query formulations
are also disregarded• document term weights are not taken into account
during the feedback loop
Term Reweighting for the Probabilistic Model (Cont.)
Evaluation of relevance feedback
• Standard evaluation method is not suitable– (i.e., recall-precision) because the relevant
documents used to reweight the query terms are moved to higher ranks
• The residual collection method– the set of all documents minus the set of feedback
documents provided by the user– because highly ranked documents are removed from
the collection, the recall-precision figures for tend to be lower than the figures for the original query
– as a basic rule of thumb, any experimentation involving relevance feedback strategies should always evaluate recall-precision figures relative to the residual collection
mqq
Automatic Strategies
• In relevance feedback, use separates the documents into two classes: relevant vs. non-relevant– An underlying notion of clustering
supporting the feedback strategy– Known relevant documents contain
terms which can be used to describe a larger cluster of relevant documents
– This can be done automatically
Automatic Strategies
• Two types of strategies– Global
• All documents in the collection are used to determine a global thesaurus-like structure which defines term relationships
– Local• The documents retrieved for a given query
are examined at query time to determine terms for query expansion
• Local clustering (Attar and Fraenkel, 1977)• Local context analysis (Xu and Croft, 1996)
Automatic Local Analysis• Definition
– local document set Dl : the set of documents retrieved by a query
– local vocabulary Vl : the set of all distinct words in Dl
– stemmed vocabulary Sl : the set of all distinct stems derived from Vl
• Local feedback strategies are based on expanding the query with terms correlated to the query terms– Such terms are those present in local clusters built from the
local document set
• Building local clusters– association clusters– metric clusters– scalar clusters
Association Clusters
• Idea– co-occurrence of stems (or terms) inside
documents
• fu,j: the frequency of a stem ku in a document dj
– local association cluster for a stem ku
• the set of k largest values in c(ku, kv)
– given a query q, find clusters for the |q| query terms
– normalized form
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Metric Clusters
• Idea– consider the distance between two terms in the same
cluster
• Definition– V(ku): the set of keywords which have the same stem form as
ku
– distance r(ki, kj)=the number of words between term ku and kv
– normalized form
)( )( ),(
1),(
u vkVi kVj jivu kkr
kkc
|)(||)(|
),(),(
vu
vuvu kVkV
kkckks
Scalar Clusters
• Idea– two stems with similar neighborhoods have
some synonymity relationships• Definition
– cu,v=c(ku, kv)– vectors of correlation values for stem ku and kv
– scalar association matrix
– scalar clusters• the set of k largest values of scalar association
),,,( ,2,1, tuuuu cccs ),,,( ,2,1, tvvvv cccs
||||,
vu
vuvu
ss
ssS
Automatic Global Analysis
• A thesaurus-like structure• Short history
– Until the beginning of the 1990s, global analysis was considered to be a technique which failed to yield consistent improvements in retrieval performance with general collections
– This perception has changed with the appearance of modern procedures for global analysis
Query Expansion based on a Similarity Thesaurus
• Idea by Qiu and Frei [1993]– Similarity thesaurus is based on term to term
relationships rather than on a matrix of co-occurrence– Terms for expansion are selected based on their
similarity to the whole query rather than on their similarities to individual query terms
• Definition– N: total number of documents in the collection– t: total number of terms in the collection– tfi,j: occurrence frequency of term ki in the document dj
– tj: the number of distinct index terms in the document dj
– itfj : the inverse term frequency for document dj
jj t
titf log
Similarity Thesaurus
• Each term is associated with a vector
– where wi,j is a weight associated to the index-document pair
• The relationship between two terms ku and kv is
– Note that this is a variation of the correlation measure used for computing scalar association matrices
),,,( ,2,1, Niii wwwki
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Term weighting vs. Term concept space
tfij
Term ki
Doc dj tfijTerm ki
Doc dj
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Query Expansion Procedure with Similarity Thesaurus
1. Represent the query in the concept space by using the representation of the index terms
2. Compute the similarity sim(q,kv) between each term kv and the whole query
3. Expand the query with the top r ranked terms according to sim(q,kv)
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Example of Similarity Thesaurus
The distance of a given term kv to the query centroid QC might be quite distinct from the distances of kv to the individual query terms
ka kb
ki
kj
kv
QC
QC={ka ,kb}
Query Expansion based on a Similarity Thesaurus
– A document dj is represented term-concept space by
– If the original query q is expanded to include all the t index terms, then the similarity sim(q, dj) between the document dj and the query q can be computed as
• which is similar to the generalized vector space model
jv u
jvu
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qkuquj
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Query Expansion based on a Statistical Thesaurus
• Idea by Crouch and Yang (1992)– Use complete link algorithm to produce
small and tight clusters– Use term discrimination value to select
terms for entry into a particular thesaurus class
• Term discrimination value– A measure of the change in space
separation which occurs when a given term is assigned to the document collection
Term Discrimination Value
• Terms– good discriminators: (terms with positive discrimination
values)• index terms
– indifferent discriminators: (near-zero discrimination values)• thesaurus class
– poor discriminators: (negative discrimination values)• term phrases
• Document frequency dfk
– dfk >n/10: high frequency term (poor discriminators)– dfk <n/100: low frequency term (indifferent
discriminators)– n/100 dfk n/10: good discriminator
Statistical Thesaurus
• Term discrimination value theory– the terms which make up a thesaurus class
must be indifferent discriminators• The proposed approach
– cluster the document collection into small, tight clusters
– A thesaurus class is defined as the intersection of all the low frequency terms in that cluster
– documents are indexed by the thesaurus classes
– the thesaurus classes are weighted by
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1 ,
C
wwt
C
i CiC
Discussion
• Query expansion – useful– little explored technique
• Trends and research issues– The combination of local analysis, global
analysis, visual displays, and interactive interfaces is also a current and important research problem
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