Lecture 3 Modeling.pdf

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Generalized Vector Space Model• {k 1 , k 2 , …, k t }: index terms in a collection• w i,j : binary weights associated with the term-document pair{k i , d j }• The patterns of term co-occurrence (inside documents) canbe represented by a set of 2 t mintermsm 1 =(0, 0, …, 0): point to documents containing none of index termsm 2 =(1, 0, …, 0): point to documents containing the index term k 1 onlym 3 =(0,1,…,0): point to documents containing the index term k 2 onlym 4 =(1,1,…,0): point to documents containing the index terms k 1 and k 2…m2t =(1, 1, …, 1): point to documents containing all the index terms• g i (m j ): return the weight {0,1} of the index term k i in theminterm m j (1 ≤ i ≤ t)3-68
Generalized Vector Space Model(Continued)mm...m122t= (1,0,...,0,0)= (0,1,...,0,0)= (0,0,...,0,1)mi• m j = 0 for i ≠ j(the set of m i are pairwise orthogonal)• m i (2 t -tuple vector) is associated with minterm m i(t-tuple vector)• e.g., m 4 is associated with m 4 containing k 1 and k 2 ,and no others• co-occurrence of index terms inside documents:dependencies among index terms3-69
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Lecture 3 Modeling3-1
Page 3 and 4:
Information Retrieval Models• Cla
Page 5 and 6:
Taxonomy of Information Retrieval M
Page 7 and 8:
Combinations of these issuesUSERTAS
Page 9 and 10:
User profile• Simplistic approach
Page 11 and 12:
Formal Definition of IR Models(cont
Page 13 and 14:
Boolean Model• The index term wei
Page 15 and 16:
Boolean Model (Continued)• advant
Page 17 and 18: Document representation in vector s
Page 19 and 20: Similarity Measure (Continued)• v
Page 21 and 22: Sample query-documentsimilarity com
Page 23 and 24: Vector Model• w i,j : a positive,
Page 25 and 26: document ranking• Similarity (i.e
Page 27 and 28: Definition of tf• N: total number
Page 29 and 30: Analysis of vector model• advanta
Page 31 and 32: Probabilistic Model (Continued)•
Page 33 and 34: Probabilistic Principle• w i,j
Page 35 and 36: 3-35independence assumption ofindex
Page 37 and 38: Initial guess• P(k i |R) is const
Page 39 and 40: Small values of V and V i• altern
Page 41 and 42: • Initial GuessProbabilistic Mode
Page 43 and 44: No Interaction with User• Retriev
Page 45 and 46: No Interaction with User• Retriev
Page 47 and 48: Interaction with User• Alternativ
Page 49 and 50: Analysis of Probabilistic Model•
Page 51 and 52: Alternative Set Theoretic Models-Fu
Page 53 and 54: Examples• Assume U={d 1 , d 2 , d
Page 55 and 56: Fuzzy OR3-55
Page 57 and 58: Fuzzy Information Retrieval• basi
Page 59 and 60: Fuzzy Information Retrieval(Continu
Page 61 and 62: ExampleQuery q=k a ∧ (k b ∨¬k
Page 63 and 64: Fuzzy Set Model3-63
Page 65 and 66: Fuzzy Set Model• Sim(q,d): Altern
Page 67: Review• Two vectors u and v are l
Page 71 and 72: t=3cccminterm m r m r vectorm 1 =(0
Page 73 and 74: Generalized Vector Space Model(Cont
Page 75 and 76: 3-7523,823,623,423,283,863,63,423,2
Page 77 and 78: asic idea• Map each document and
Page 79 and 80: Singular Value DecompositionA∈R(1
Page 81 and 82: A=QDQtAQ=tQDQ Q=QDwhere Q=[ q q21Kq
Page 83 and 84: M=tM= ( K S D= ( DSDSM M=K St22: do
Page 85 and 86: Ranking in LSI• query: a pseudo-d
Page 87 and 88: Non-Overlapping Lists• divide the
Page 89 and 90: Inverted Files• File is represent
Page 91 and 92: Inverted-file process (Continued)
Page 93 and 94: Extensions of Inverted Index Operat
Page 95 and 96: Model Based on Proximal Nodes• hi
Page 97 and 98: Model Based on Proximal Nodes(Conti
Page 99 and 100: Models for Browsing• Flat organiz
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