4. Let’s define three events: 1. A as “draw 47 resistor 2. B as “draw” a resistor with 5% 3. C as “draw” a “100 resistor P(A) = P(47 ) = 44/100 P(B) = P(5%) = 62/100 P(C) = P(100 ) = 32 /100 The joint probabilities are: P(A B) = P(47 5%) = 28/100 P(A C) = P(47 100 ) = 0 P(B C) = P(5% 100 ) = 24/100 I f we use them the cond. prob. : Tolerance Resistance ( ) 5% 10% Total 22- 10 14 24 47- 28 26 44 100- 24 8 32 Total: 62 38 100
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9. User Information Need Documents Document Representation Query Representation How to match? In traditional IR systems, matching between each document and query is attempted in a semantically imprecise space of index terms. Probabilities provide a principled foundation for uncertain reasoning. Can we use probabilities to quantify our uncertainties? Uncertain guess of whether document has relevant content Understanding of user need is uncertain
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14. Let x be a document in the collection. Let R represent relevance of a document w.r.t. given (fixed) query and let NR represent non-relevance. p( x|R ), p( x|NR ) - probability that if a relevant (non-relevant) document is retrieved, it is x . Need to find p( R|x) - probability that a document x is relevant. p( R) ,p( NR ) - prior probability of retrieving a (non) relevant document R={0,1} vs. NR/R
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24. All matching terms Non-matching query terms All matching terms All query terms
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34. Gloom (g) Finals (f) No Sleep (n) Triple Latte (t) Project Due (d)
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37. I - goal node Document Network Query Network Large, but Compute once for each document collection Small, compute once for every query d1 d n d2 t1 t2 t n r1 r2 r3 r k d i - documents t i - document representations r i - “concepts” I q2 q1 c m c2 c1 c i - query concepts q i - high-level concepts
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39. d 1 d 2 r 1 r 3 c 1 c 3 q 1 q 2 i r 2 c 2 Document Network Query Network Documents Terms/Concepts Concepts Query operators ( AND/OR/NOT ) Information need
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41. Hamlet Macbeth reason double reason two OR NOT User query trouble trouble Document Network Query Network
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