Lecture 3 Modeling.pdf

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Basic Concepts of Classic IR• index terms (usually nouns): index and summarize• weight of index terms• Definition– K={k 1 , …, k t }: a set of all index terms– w i,j : a weight of an index term k i of a document d j– d j =(w 1,j , w 2,j , …, w t,j ): an index term vector for thedocument d j– g i (d j )= w w i,ji,j associated with (k i ,d j ) tells us nothingabout w i+1,j associated with (k i+1 ,d j )• assumption– index term weights are mutually independentThe terms computer and network in the area of computer networks3-12
Boolean Model• The index term weight variables are allbinary, i.e., w i,j ∈{0,1}• A query q is a Boolean expression (and, or, not)• q dnf : the disjunctive normal form for q• q cc : conjunctive components of q dnf• sim(d j ,q): similarity of d j to q– 1: if ∃q cc | (q cc ∈q dnf ∧(∀k i , g i (d j )=g i (q cc ))– 0: otherwisedj is relevant to q3-13
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: Formal Definition of IR Models(cont
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 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
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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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