Query Operations:Automatic Local Analysis

Introduction

Difficulty of formulating user queries– Insufficient knowledge of the collection– Insufficient knowledge of the retrieval

environment

Query reformulation– two basic steps

• query expansion– Expanding the original query with new terms

• term reweighting– Reweighting the terms in the expanded query

Automatic Relevance Feedback

Basic idea– clustering: known relevant documents contain

terms which can be used to describe a larger cluster of relevant documents.

– obtain a description for a larger cluster of relevant documents automatically.

• identifying terms which are related to the query terms

• synonyms, stemming variations, terms which are close to the query terms in the text, etc.

– global analysis vs. local analysis

Global vs. Local Analysis

Global analysis– all documents in the collection are used to determine

a global thesaurus-like structure which defines term relationships

– this structure can be shown to the user who selects clusters or terms for query expansion

Local analysis– the documents retrieved for a given query q are

examined at query time to determine terms for query expansion

– local clustering and local context analysis: without assistance from the user

Local Clustering

Operate solely on the documents retrieved for the current query

Valuable because term distributions are not uniform across topic areas– Distinguishing terms are different for different topics– Global techniques cannot take these differences into

account.

Requires significant run-time computation– Not for Web search engines due to cost– Useful in intranet environments and for specialized

document collections

Local ClusteringInitially use stemming to group terms

– For stem s = polish– V(s) = {polish, polishing, polished}

Definitions– q: query– Dl : local document set (retrieved documents)– Vl : vocabulary of Dl

– Sl : set of distinct stems for Vl

Three types of clusters– association clusters– metric clusters– scalar clusters

Association Clusters

Idea: Terms which co-occur frequently inside documents likely relate the same concept.

Simple computation based on the frequency of co-occurrence of terms inside documents

– correlation between the stems

Association Clusters

Definitions– Matrix m has |Sl| rows and |Dl| columns

• mij = fsi,j (frequency of stem si in document dj)

– Correlation cu,v is computed as:

– Matrix s = mmt

• Unnormalized– su,v = cu,v

• Normalized– su,v = cu,v / (cu,u + cv,v – cu,v)

Association Clusters

Selecting Clusters– Normally want stems for each query term– Need clusters to be small in order to retain

focus– Select fixed size of clusters, n

To expand query q– Construct cluster for each query term q

• Identify sq, the stem for query term q• For stem sq, select the top n values sq,v

– Union of all query term clusters is expanded query

Metric Clusters

Two terms which are near one another are more likely to be correlated than two terms which occur far apart– factor in the distance between two terms in

the computation of their correlation factor

Same as Association Clusters except for the computation of cu,v

Metric Clusters

Same as Association Clusters except for the computation of cu,v

Correlation between the stems su and sv

Where r (ki, kj) = distance between keywords in the same document

This is unnormalized. Can be normalized.

Scalar Clusters

Idea: two stems with similar neighborhoods have some synonymity relationship

The relationship is indirect or induced by the neighborhood.

Quantifying such neighborhood relationships– Arrange all correlation values su,i in a vector

– Arrange all correlation values sv,i in another vector

– Compare these vectors through a scalar measure– The cosine of the angle between the two vectors is a

popular scalar similarity measure.

Clustering Approaches

In practice:– Metric clusters outperform association

clusters– Using a combination of normalized and

unnormalized correlation factors can be beneficial

• Unnormalized factors tend to group stems due to large frequencies

• Normalized factors tend to group stems which are more rare

Clustering Approaches

Local approaches use the frequencies and correlations of terms and stems within the set of documents retrieved– These frequencies and correlations may not

be representative of the overall collection– How is this good? How is this bad?

Query Operations:Automatic Global Analysis

Motivation

Methods of local analysis extract information from local set of documents retrieved to expand the query

An alternative is to expand the query using information from the whole set of documents

Until the beginning of the 1990s these techniques failed to yield consistent improvements in retrieval performance

Now, with moderns variants, sometimes based on a thesaurus, this perception has changed

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

Similarity Thesaurus

The similarity thesaurus is based on term-to-term relationships rather than on a matrix of co-occurrence.– These relationships 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

Similarity Thesaurus vs. Vector Model

The frequency factor:– In vector model

f (i,j) = freq ( term ki in doc dj ) / freq ( most common term in dj )

– In similarity thesaurusf (i,j) = freq ( term ki in doc dj ) / freq ( doc where term ki appears

most)– Normalized based on document where the term appears most.

The inverse frequency factor:– In vector model

Idf (i) = log (# of docs in collection / # of docs with term ki)

– In similarity thesaurusItf (j) = log (# of terms in collection / # of terms in doc dj)

– Calculates how good a discriminator is this document?

Similarity ThesaurusDefinitions:

– 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

The relationship between two terms ku and kv is computed as a correlation factor cu,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

The computation is expensive but only has to be computed 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: 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)

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– These 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.

Complete Link Algorithm

Document clustering algorithm– 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– Use of minimum ensures small, focussed clusters

Generating the Thesaurus

Given the document cluster hierarchy for the whole collection– Which clusters become classes?– Which terms represent classes?

Answers based on three parameters specified by operator based on characteristics of the collection– TC: Threshold class– NDC: Number of documents in class– MIDF: Minimum inverse document frequency

Selecting Thesaurus Classes

TC is the minimum similarity between two subclusters for the parent to be considered a class.– A high value makes classes smaller and

more focussed.

NDC is an upper limit on the size of clusters.– A low value of NDC restricts the selection to

smaller, more focussed clusters

Picking Terms for Each Class

Consider the set of documents in each class selected above

Only the lower frequency terms are used for the thesaurus classes

The parameter MIDF defines the minimum inverse document frequency for a term to represent the thesaurus class

Initializing 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

– A low value of TC yields too few classes

NDC is easier to set once TC is set

MIDF can be difficult to set

Query Expansion with Statisitcal Thesaurus

Adding Terms:– Use the terms in same class at terms in

query

Weights of new terms can be based on both – the original query term weights (if any)

and – on the degree to which a term represents

the class of the query term

Conclusions

Automatically generated thesaurus is a method to expand queries

Thesaurus generation is expensive but it is executed only once

Query expansion based on similarity thesaurus uses term frequencies to expand the query

Query expansion based on statistical thesaurus uses document clustering and needs well defined parameters