Vector space model of information retrieval

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The document discusses different theories used in information retrieval systems. It describes cognitive or user-centered theories that model human information behavior and structural or system-centered theories like the vector space model. The vector space model represents documents and queries as vectors of term weights and compares similarities between queries and documents. It was first used in the SMART information retrieval system and involves assigning term vectors and weights to documents based on relevance.

BY
NANTHINI R O
II – MLIS
PONDICHERRY UNIVERSITY

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Theory based approach to design various
aspects of information retrieval systems
Based on a set of principles and assumptions

Theory drives experiment by suggesting new
ways and means of doing tests
Experiment drives theory by justifying or
helping to improve the model

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Cognitive or user centered
◦ Human information behaviour models
◦ Eg: Wilson’s model, Dervin’s model, Ellis’s model,
Bates’s model, Kulthau’s model, etc...

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Structural or system centered
◦ Classical models based on logical and mathematical
principles
◦ Eg: Boolean search model, Vector Space model,
probabilistic model, etc...

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Also called as ‘term vector model’ or ‘vector
processing model’
Represents both documents and queries by term
sets and compares global similarities between
queries and documents
used in information filtering, information
retrieval, indexing and relevancy rankings

first use was in the SMART Information Retrieval
System

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term vectors are assigned for the keywords of the
documents and weights are provided according to
relevance

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to compare different texts and retrieve relevant
records similar to the queries

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terms are single words, keywords, or longer phrases

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If words are chosen to be the terms, the
dimensionality of the vector is the number of words
in the vocabulary (the number of distinct words occurring in the corpus)

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BASICS: (i and j are 2 documents, k – term, t – last term)

◦ Denotes the sum of the weights of all properties of
a vector

◦ Denotes the sum of products of corresponding term
weights for two vectors

◦ Denotes the sum of minimum component weights
of the corresponding two vectors
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Similarity coefficients
◦ The Dice Coefficient

◦ The Jaccard Coefficient

acc. to Salton and McGill

Let the weights for the index terms assigned to two
documents i and j be as follows:

Doci = 3,2,1,0,0,0,1,1
Docj = 1,1,1,0,0,1,0,0

= 2 [(3*1)+(2*1)+(1*1)+(0*0)+(0*0)+(0*1)+(1*0)+(1*0)]
(3+2+1+0+0+0+1+1)+(1+1+1+0+0+1+0+0)
=12/12 = 1

Vector space model of information retrieval

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Vector space model of information retrieval

1. BY NANTHINI R O II – MLIS PONDICHERRY UNIVERSITY

2.     Theory based approach to design various aspects of information retrieval systems Based on a set of principles and assumptions Theory drives experiment by suggesting new ways and means of doing tests Experiment drives theory by justifying or helping to improve the model

3.  Cognitive or user centered ◦ Human information behaviour models ◦ Eg: Wilson’s model, Dervin’s model, Ellis’s model, Bates’s model, Kulthau’s model, etc...  Structural or system centered ◦ Classical models based on logical and mathematical principles ◦ Eg: Boolean search model, Vector Space model, probabilistic model, etc...

4.     Also called as ‘term vector model’ or ‘vector processing model’ Represents both documents and queries by term sets and compares global similarities between queries and documents used in information filtering, information retrieval, indexing and relevancy rankings first use was in the SMART Information Retrieval System

5.  term vectors are assigned for the keywords of the documents and weights are provided according to relevance  to compare different texts and retrieve relevant records similar to the queries  terms are single words, keywords, or longer phrases  If words are chosen to be the terms, the dimensionality of the vector is the number of words in the vocabulary (the number of distinct words occurring in the corpus)

6.  BASICS: (i and j are 2 documents, k – term, t – last term) ◦ Denotes the sum of the weights of all properties of a vector ◦ Denotes the sum of products of corresponding term weights for two vectors

7. ◦ Denotes the sum of minimum component weights of the corresponding two vectors  Similarity coefficients ◦ The Dice Coefficient ◦ The Jaccard Coefficient acc. to Salton and McGill

8. Let the weights for the index terms assigned to two documents i and j be as follows: Doci = 3,2,1,0,0,0,1,1 Docj = 1,1,1,0,0,1,0,0

9. = 2 [(3*1)+(2*1)+(1*1)+(0*0)+(0*0)+(0*1)+(1*0)+(1*0)] (3+2+1+0+0+0+1+1)+(1+1+1+0+0+1+0+0) =12/12 = 1

10. = 6/(12-6) = 1

Vector space model of information retrieval

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