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Information Retrieval : 15
Alternative Algebraic Models
Prof Neeraj Bhargava
Vaibhav Khanna
Department of Computer Science
School of Engineering and Systems Sciences
Maharshi Dayanand Saraswati University Ajmer
Alternative Algebraic Models
• Generalized Vector Model
• Latent Semantic Indexing
• Neural Network Model
Generalized Vector Model
• Classic models enforce independence of index
terms
• In the generalized vector space model, two
index term vectors might be non-orthogonal
Key Idea
• As before, let wi,j be the weight associated with [ki, dj ] and V =
{k1, k2, . . ., kt} be the set of all terms
• If the wi,j weights are binary, all patterns of occurrence of
terms within docs can be represented by minterms:
Information retrieval 15 alternative algebraic models
• Pairwise orthogonality among the ~mr vectors does
not imply independence among the index terms
• On the contrary, index terms are now correlated by the
~mr vectors
– For instance, the vector ~m4 is associated with the
minterm m4 = (1, 1, . . . , 0)
– This minterm induces a dependency between terms k1
and k2
– Thus, if such document exists in a collection, we say that
the minterm m4 is active
• The model adopts the idea that co-occurrence of terms
induces dependencies among these terms
Information retrieval 15 alternative algebraic models
Computation of ci,r
Computation of
Computation of Document Vectors
Conclusions
• Model considers correlations among index
terms
• Not clear in which situations it is superior to
the standard Vector model
• Computation costs are higher
• Model does introduce interesting new ideas
Assignment
• Explain the Generalized Vector Model of IR
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Information retrieval 15 alternative algebraic models

  • 1. Information Retrieval : 15 Alternative Algebraic Models Prof Neeraj Bhargava Vaibhav Khanna Department of Computer Science School of Engineering and Systems Sciences Maharshi Dayanand Saraswati University Ajmer
  • 2. Alternative Algebraic Models • Generalized Vector Model • Latent Semantic Indexing • Neural Network Model
  • 3. Generalized Vector Model • Classic models enforce independence of index terms • In the generalized vector space model, two index term vectors might be non-orthogonal
  • 4. Key Idea • As before, let wi,j be the weight associated with [ki, dj ] and V = {k1, k2, . . ., kt} be the set of all terms • If the wi,j weights are binary, all patterns of occurrence of terms within docs can be represented by minterms:
  • 6. • Pairwise orthogonality among the ~mr vectors does not imply independence among the index terms • On the contrary, index terms are now correlated by the ~mr vectors – For instance, the vector ~m4 is associated with the minterm m4 = (1, 1, . . . , 0) – This minterm induces a dependency between terms k1 and k2 – Thus, if such document exists in a collection, we say that the minterm m4 is active • The model adopts the idea that co-occurrence of terms induces dependencies among these terms
  • 11. Conclusions • Model considers correlations among index terms • Not clear in which situations it is superior to the standard Vector model • Computation costs are higher • Model does introduce interesting new ideas
  • 12. Assignment • Explain the Generalized Vector Model of IR