1 vector space model rong jin. 2 basic issues in a retrieval model how to represent text objects...

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Vector Space Model

Rong Jin

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Basic Issues in A Retrieval Model

How to represent text

objects

What similarity function should

be used?

How to refine query according to users’

feedbacks?

3

Basic Issues in IR How to represent queries? How to represent documents? How to compute the similarity between

documents and queries? How to utilize the users’ feedbacks to

enhance the retrieval performance?

4

IR: Formal Formulation Vocabulary V={w1, w2, …, wn} of language Query q = q1,…,qm, where qi V Collection C= {d1, …, dk}

Document di = (di1,…,dimi), where dij V

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)”

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Computing R(q) Strategy 1: Document selection

Classification function f(d,q) {0,1} Outputs 1 for relevance, 0 for irrelevance

R(q) is determined as a set {dC|f(d,q)=1} System must decide if a doc is relevant or not

(“absolute relevance”) Example: Boolean retrieval

Document Selection Approach

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++

+ +-- -

- - - -

- - - -

-

- - +- -

True R(q)Classifier

C(q)

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Computing R(q) Strategy 2: Document ranking

Similarity function f(d,q) Outputs a similarity between document d and query q

Cut off The minimum similarity for document and query to be

relevant

R(q) is determined as the set {dC|f(d,q)>} System must decide if one doc is more likely to

be relevant than another (“relative relevance”)

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Document Selection vs. Ranking

++

+ +-- -

- - - -

- - - -

-

- - +- - Doc Ranking

f(d,q)=?

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)

True R(q)

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Document Selection vs. Ranking

++

+ +-- -

- - - -

- - - -

-

- - +- -

Doc Rankingf(d,q)=?

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)

---

1Doc Selection

f(d,q)=?

++

++

--+

-+

--

- --0

R’(q)

True R(q)

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Ranking is often preferred Similarity function is more general than

classification function The classifier is unlikely to be accurate

Ambiguous information needs, short queries Relevance is a subjective concept

Absolute relevance vs. relative relevance

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Probability Ranking Principle As stated by Cooper

Ranking documents in probability maximizes the utility of IR systems

“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 as 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.”

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Vector Space Model Any text object can be represented by a term vector

Examples: Documents, queries, sentences, …. A query is viewed as a short document

Similarity is determined by relationship between two vectors e.g., the cosine of the angle between the vectors, or the

distance between vectors The SMART system:

Developed at Cornell University, 1960-1999 Still used widely

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Vector Space Model: illustration

Java Starbuck Microsoft

D1 1 1 0

D2 0 1 1

D3 1 0 1

D4 1 1 1

Query 1 0.1 1

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Vector Space Model: illustration

Java

Microsoft

StarbucksD2 ? ?

D1

? ?

D3

? ?

Query

D4 ? ?

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Vector Space Model: Similarity Represent both documents and queries by word histogram

vectors n: the number of unique words A query q = (q1, q2,…, qn)

qi: occurrence of the i-th word in query

A document dk = (dk,1, dk,2,…, dk,n)

dk,i: occurrence of the the i-th word in document

Similarity of a query q to a document dk q

dk

Some Background in Linear Algebra Dot product (scalar product)

Example:

Measure the similarity by dot product

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nknkk dqdqdq ,2,21,1 ... kdq

26453211

]4,3,1[],5,2,1[

6051241

]0,1,4[],5,2,1[

k

k

k

k

dq

dq

dq

dq

kdq

Some Background in Linear Algebra Length of a vector

Angle between two vectors

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),( dq

q

dk

2,

22,

21,

222

21 ...,... nkkkn dddqqq kdq

2,

22,

21,

222

21

,2,21,1

......

...

)),(cos(

nkkkn

nknkk

dddqqq

dqdqdq

k

kk dq

dqdq

Some Background in Linear Algebra Example:

Measure similarity by the angle between vectors

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97.0431521

453211)),(cos(

]4,3,1[],5,2,1[

27.0014521

051241)),(cos(

]0,1,4[],5,2,1[

222222

222222

k

k

k

k

dq

dq

dq

dq

),( dq

q

dk

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Vector Space Model: Similarity Given

A query q = (q1, q2,…, qn) qi: occurrence of the i-th word in query

A document dk = (dk,1, dk,2,…, dk,n) dk,i: occurrence of the the i-th word in

document

Similarity of a query q to a document dk

q

dk

)),(cos(

...

),(sim

,2,21,1

kkk dqdqdq

nknkk

k

dqdqdq

dq

),( dq

2,

22,

21,

222

21

,2,21,1

......

...

)),(cos(),(sim'

nkkkn

nknkk

k

dddqqq

dqdqdq

dq

k

kk dq

dqdq

Vector Space Model: Similarity

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q

dk

26453211

]4,3,1[],5,2,1[

40850201

]8,0,0[],5,2,1[

k

k

k

k

dq

dq

dq

dq

Vector Space Model: Similarity

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97.0431521

453211)),(cos(

]4,3,1[],5,2,1[

913.0800521

850201)),(cos(

]8,0,0[],5,2,1[

222222

222222

k

k

k

k

dq

dq

dq

dq

),( dq

q

dk

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Term Weighting

wk,i: the importance of the i-th word for document dk

Why weighting ? Some query terms carry more information

TF.IDF weighting TF (Term Frequency) = Within-doc-frequency IDF (Inverse Document Frequency) TF normalization: avoid the bias of long documents

1 ,1 2 ,2 ,( , )k k k n k nsim q d q d q d q d

1 ,1 2,1 ,2 ,,2 ,( , )k k kk k kn nk nsim q d q d q d q dw w w

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TF Weighting A term is important if it occurs frequently in document Formulas:

f(t,d): term occurrence of word ‘t’ in document d Maximum frequency normalization:

( , )( , ) 0.5 0.5

MaxFreq( )

f t dTf t d

d

Term frequency normalization

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TF Weighting A term is important if it occurs frequently in document Formulas:

f(t,d): term occurrence of word ‘t’ in document d “Okapi/BM25 TF”:

( , )( , )

( )( , ) 1

_

f t dTf t d

doclen df t d

avg docl

k

be

bn

k

Term frequency normalization

doclen(d): the length of document d

avg_doclen: average document length

k,b: predefined constants

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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)

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TF Normalization

Norm. TF

Raw TF

“Pivoted normalization”( , )

( , )( )

( , ) 1_

f t dTf t d

doclen df t d

avg docl

k

be

bn

k

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IDF Weighting A term is discriminative if it occurs only in a

few documents Formula:

IDF(t) = 1+ log(n/m)n – total number of docsm -- # docs with term t (doc freq)

Can be interpreted as mutual information

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TF-IDF Weighting TF-IDF weighting :

The importance of a term t to a document d

weight(t,d)=TF(t,d)*IDF(t)

Freq in doc high tf high weight Rare in collection high idf high weight


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TF-IDF Weighting TF-IDF weighting :

The importance of a term t to a document d

weight(t,d)=TF(t,d)*IDF(t)

Freq in doc high tf high weight Rare in collection high idf high weight

Both qi and dk,i arebinary values, i.e. presence and absence of a word in query and document.


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Problems with Vector Space Model Still limited to word based matching

A document will never be retrieved if it does not contain any query word

How to modify the vector space model ?

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Choice of Bases

Java

Microsoft

Starbucks

D1

Q

D

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Choice of Bases

Java

Microsoft

Starbucks

D1

Q

D

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Choice of Bases

Java

Microsoft

Starbucks

D1

Q

D

D’

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Choice of Bases

Java

Microsoft

Starbucks

D1

Q

D

D’

Q’

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Choice of Bases

Java

Microsoft

Starbucks

D1

D’

Q’

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Choosing Bases for VSM Modify the bases of the vector space

Each basis is a concept: a group of words Every document is a vector in the concept space

A1

A2

c1 c2 c3 c4 c5 m1 m2 m3 m4

A1 1 1 1 1 1 0 0 0 0

A2 0 0 0 0 0 1 1 1 1

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Each basis is a concept: a group of words Every document is a mixture of concepts

A1

A2

c1 c2 c3 c4 c5 m1 m2 m3 m4

A1 1 1 1 1 1 0 0 0 0

A2 0 0 0 0 0 1 1 1 1

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Each basis is a concept: a group of words Every document is a mixture of concepts

How to define/select ‘basic concept’? In VS model, each term is viewed as an

independent concept

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Basic: Matrix Multiplication

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Basic: Matrix Multiplication

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Linear Algebra Basic: Eigen Analysis Eigenvectors (for a square mm matrix S)

Example

eigenvalue(right) eigenvector

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Linear Algebra Basic: Eigen Analysis

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12S

2/1

2/1,1 eigenvalue second the

2/1

2/1,3 eigenvaluefirst the

22

11

v

v

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Linear Algebra Basic: Eigen Decomposition

1 1 1 12 1 3 02 2 2 21 2 1 1 0 1 1 1

2 2 2 2

S

S = U * * UT

,1,3 2/1

2/1 ,

2/1

2/12121

vv

44


1 1 1 12 1 3 02 2 2 21 2 1 1 0 1 1 1

2 2 2 2

S

S = U * * UT

,1,3 2/1

2/1 ,

2/1

2/12121

vv

45


1 1 1 12 1 3 02 2 2 21 2 1 1 0 1 1 1

2 2 2 2

S

This is generally true for symmetric square matrix

Columns of U are eigenvectors of S

Diagonal elements of are eigenvalues of S

S = U * * UT

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Singular Value Decomposition

TVUA

mm mn V is nn

For an m n matrix A of rank r there exists a factorization(Singular Value Decomposition = SVD) as follows:

The columns of U are left singular vectors.

The columns of V are right singular vectors

is a diagonal matrix with singular values

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Singular Value Decomposition Illustration of SVD dimensions and sparseness

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Low Rank Approximation Approximate matrix with the largest singular

values and singular vectors

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Latent Semantic Indexing (LSI)Computation: using single value decomposition (SVD) with the first m largest singular values and singular vectors, where m is the number of concepts

Rep. of Concepts in term space

Concept

Concept

Rep. of concepts in document space

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Finding “Good Concepts”

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54.20

034.3X X

SVD: Example: m=2

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54.20

034.3X X

SVD: Example: m=2

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54.20

034.3X X

SVD: Example: m=2

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54.20

034.3X X

SVD: Example: m=2

5

476.0

34.3

54.2

59

SVD: Orthogonality

54.20

034.3X X

u1 u2 · = 0

v1

v2

v1 · v2 = 0

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54.20

034.3

X X

SVD: Properties

rank(S): the maximum number of either row or column vectors within matrix S that are linearly independent.

SVD produces the best low rank approximation

X’: rank(X’) = 2X: rank(X) = 9

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SVD: Visualization

X =

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SVD: Visualization

SVD tries to preserve the Euclidean distance of document vectors