Lecture 3 Modeling.pdf

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Probabilistic Principle• Given a user query q and a document d j in thecollection, the probabilistic model estimates theprobability that user will find d j relevant• assumptions– The probability of relevance depends on query anddocument representations only– There is a subset of all documents which the userprefers as the answer set for the query q• Given a query, the probabilistic model assigns toeach document dj a measure of its similarity to thequeryP(dP(djjrelevant − to q)nonrelevant − to q)3-32
Probabilistic Principle• w i,j ∈{0,1}, w i,q ∈{0,1}: the index term weight variablesare all binary non-relevant• q: a query which is a subset of index terms• R: the set of documents known to be relevant• R (complement of R): the set of non-relevant documents• P(R|d j ): the probability that the document d j is relevantto the query q• P(R|d j ): the probability that d j is non-relevant to q3-33
Page 1 and 2: 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: Probabilistic Model (Continued)•
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 and 68: Review• Two vectors u and v are l
Page 69 and 70: Generalized Vector Space Model(Cont
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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