Lecture 3 Modeling.pdf

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Consider only the s largest singular values of Sλ 1λ 2λ n00...λ 1 ≥λ 2 ≥… ≥λ n ≥ 0The resultant M s matrix is the matrix of rank s which is closestto the original matrix M in the least square sense.t 由概念分群來說明 :s K s S s D s太細 - 各個 index term 代表不同的概念(s
Ranking in LSI• query: a pseudo-document in the original Mterm-document– query is modeled as the document with number 0– M st M s : the ranks of all documents w.r.t this queryM==tsDs( DMSsssSs=Kts( KK)( DsssSSSsss)DtDtsts)t=KDssSSssDStssDts(i,j) qualifies the relationship betweendocuments d i and d j When i = 0,it denotes similarity between q and documents3-85
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Lecture 3 Modeling3-1
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Information Retrieval Models• Cla
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Taxonomy of Information Retrieval M
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Combinations of these issuesUSERTAS
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User profile• Simplistic approach
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Formal Definition of IR Models(cont
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Boolean Model• The index term wei
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Boolean Model (Continued)• advant
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Document representation in vector s
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Similarity Measure (Continued)• v
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Sample query-documentsimilarity com
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Vector Model• w i,j : a positive,
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document ranking• Similarity (i.e
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Definition of tf• N: total number
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Analysis of vector model• advanta
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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 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: M=tM= ( K S D= ( DSDSM M=K St22: do
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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