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확률모델

on Nov 02, 2009

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정보검색시스템 강의노트_강승식교수님

정보검색시스템 강의노트_강승식교수님

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확률모델Presentation Transcript

• 2.5.4 Probabilistic Model
• Motivation
• Attempt to capture the IR problem within a probabilistic framework
• Assumption (Probabilistic Principle)
• Probability of relevance depends on the query and the document representations only
• There is a subset of all documents which the user prefers as the answer set for the query q
• Such an ideal answer set is labeled R and should maximize the overall probability of relevance to the user
• Documents in the set R are predicted to be relevant to the query
• Documents not in this set are predicted to be non-relevant
• Probabilistic Model
• Definition
23 Using Bayes’ rule
• Probabilistic Model
• Definition (Cont.)
23 Assuming independence of index terms Taking logarithms, ignoring constants,
• Probabilistic Model
• Initial Probability
• Improving Probability
23 Adjustment the problems for small values of V and V i
• Probabilistic Model
• Documents are ranked in decreasing order of their probability of being relevant
• Guess the initial separation of documents into relevant and non-relevant sets
• Binary weights
• Does not take into account the frequency with which an index term occurs inside a document
• Independence assumption for index terms
23
• 2.5.5 Brief Comparison of Classic Models
• Boolean Model
• Weakest classic method
• Inability to recognize partial matches
• Vector Model
• Popular retrieval model
• Vector Model v.s Probabilistic Model
• Croft
• Probabilistic model provides a better retrieval performance
• Salton, Buckley
• Vector model is expected to outperform the probabilistic model with general collections
23
• 참고 : Probabilistic Model Example of Probabilistic Retrieval Model
• Query : q(k 1 , k 2 )
• 5 documents
• Assume d 2 and d 4 are relevant
23 d 1 d 2 d 3 d 4 d 5 k 1 1 0 1 1 0 k 2 0 1 0 1 1
• Probabilistic Model Example of Probabilistic Model
• Q: “gold silver truck”
• D1: “Shipment of gold damaged in a fire.”
• D2: “Delivery of silver arrived in a silver truck.”
• D3: “Shipment of gold arrived in a truck.”
• Assume that documents D2 and D3 are relevant to the query
• N= number of documents in the collection
• R= number of relevant documents for a given query Q
• n= number of documents that contain term t
• r= number of relevant documents that contain term t
23 variable Gold Silver Truck N 3 3 3 R 2 2 2 n 2 1 2 r 1 1 2
• Probabilistic Model Example of Probabilistic Model (Cont.)
• Computing Term Relevance Weight
• Similarity Coefficient for each document
• D1: -0.477, D2: 1.653, D3: 0.699
23
• 참고 : Review of Probability Theory
• Try to predict whether or not a baseball team (e.g. LA Dodgers) will win one of its games
• Let’s assume the independence of evidences
• By Bayes’ Theorem
23 We can’t calculate it, so… Probability to lose
• Probabilistic Model Review of Probability Theory (Cont.)
• Independent Assumption
• Making Substitutions
23
• Probabilistic Model Review of Probability Theory (Cont.)
• Making Substitutions (Cont.)
The probability of LA Dodgers will win its game on sunny days when Chanho plays