《网络搜索和挖掘关键技术 Web Search and Mining》课程教学资源（PPT讲稿）Lecture 11 Probabilistic Information Retrieval

Probabilistic Information Retrieval Web Search and Mining Lecture 11: Probabilistic Information Retrieval

Probabilistic Information Retrieval 1 Lecture 11: Probabilistic Information Retrieval Web Search and Mining

Probabilistic Information Retrieval Recap of the last lecture Improving search results Especially for high recall. E. g searching for aircraft so it matches with plane; thermodynamic with heat Options for improving results Global methods Query expansion Thesauri Automatic thesaurus generation Global indirect relevance feedback ■Loca| methods Relevance feedback Pseudo relevance feedback

Probabilistic Information Retrieval 2 Recap of the last lecture ▪ Improving search results ▪ Especially for high recall. E.g., searching for aircraft so it matches with plane; thermodynamic with heat ▪ Options for improving results… ▪ Global methods ▪ Query expansion ▪ Thesauri ▪ Automatic thesaurus generation ▪ Global indirect relevance feedback ▪ Local methods ▪ Relevance feedback ▪ Pseudo relevance feedback

Probabilistic Information Retrieval Probabilistic relevance feedback Rather than reweighting in a vector space If user has told us some relevant and some irrelevant documents then we can proceed to build a probabilistic classifier, such as a Naive bayes model P(tkIR)=Drk/D P(tkINr) =Dnrk I /Dnr tk is a term; D is the set of known relevant documents Drk is the subset that contain ti D. is the set of known irrelevant documents D is the subset that contain t

Probabilistic Information Retrieval 3 Probabilistic relevance feedback ▪ Rather than reweighting in a vector space… ▪ If user has told us some relevant and some irrelevant documents, then we can proceed to build a probabilistic classifier, such as a Naive Bayes model: ▪ P(tk|R) = |Drk| / |Dr| ▪ P(tk|NR) = |Dnrk| / |Dnr| ▪ tk is a term; Dr is the set of known relevant documents; Drk is the subset that contain tk ; Dnr is the set of known irrelevant documents; Dnrk is the subset that contain tk

Probabilistic Information Retrieval Why probabilities in IR? User Understanding Query Information Need Representation of user need is un certain How to match? Uncertain guess of Document Documents whether docum ent Representation has relevant content In traditional IR systems, matching between each document and query is attempted in a semantically imprecise space of index terms Probabilities provide a princi pled foundation for un certain reasoning Can we use probabilities to guantify our uncertainties?

Probabilistic Information Retrieval 4 Why probabilities in IR? 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

Probabilistic Information Retrieval Probabilistic IR topics Classical probabilistic retrieval model Probability ranking principle, etc Binary independence model Bayesian networks for text retrieval Language model approach to IR An important emphasis in recent work Probabilistic methods are one of the oldest but also one of the currently hottest topics in /R Traditionally: neat ideas, but they ve never won on performance. It may be different now

Probabilistic Information Retrieval 5 Probabilistic IR topics ▪ Classical probabilistic retrieval model ▪ Probability ranking principle, etc. ▪ Binary independence model ▪ Bayesian networks for text retrieval ▪ Language model approach to IR ▪ An important emphasis in recent work ▪ Probabilistic methods are one of the oldest but also one of the currently hottest topics in IR. ▪ Traditionally: neat ideas, but they’ve never won on performance. It may be different now

Probabilistic Information Retrieval The document ranking problem We have a collection of documents User issues a query a list of documents needs to be returned Ranking method is core of an IR system In what order do we present documents to the user? We want the best document to be first second best second, etc Idea: Rank by probability of relevance of the document w.r.t information need P(relevant document, query

Probabilistic Information Retrieval 6 The document ranking problem ▪ We have a collection of documents ▪ User issues a query ▪ A list of documents needs to be returned ▪ Ranking method is core of an IR system: ▪ In what order do we present documents to the user? ▪ We want the “best” document to be first, second best second, etc…. ▪ Idea: Rank by probability of relevance of the document w.r.t. information need ▪ P(relevant|documenti , query)

Probabilistic Information Retrieval Probability Basics Recall a few probability basics or events a and b Bayes Rule p(a,b)=p(anb)=p(a bp(b)=p((a p(a bp(b)=p(b ap(ay P(alb)=p(bla)p(a=sp(blapla prior p(b)∑ pbx(x) Posterion x=aa Odds: O(a)=p(a)

Probabilistic Information Retrieval 7 Recall a few probability basics ▪ For events a and b: ▪ Bayes’ Rule ▪ Odds:  = = = = =  = = x a a p b x p x p b a p a p b p b a p a p a b p a b p b p b a p a p a b p a b p a b p b p b a p a , ( | ) ( ) ( | ) ( ) ( ) ( | ) ( ) ( | ) ( | ) ( ) ( | ) ( ) ( , ) ( ) ( | ) ( ) ( | ) ( ) 1 ( ) ( ) ( ) ( ) ( ) p a p a p a p a O a − = = Posterior Prior Probability Basics

Probabilistic Information Retrieval Probability Ranking Principle The probability ranking principle If a reference retrieval system s response to each request is a ranking of the documents in the collection in order of decreasing probability of relevance to the user who submitted the request, where the probabilities are estimated as accurately as possible on the basis of whatever data have been made available to the system for this purpose, the overall effectiveness of the system to its user will be the best that is obtainable on the basis of those data [1960s/1970s]S Robertson, W.S. Cooper, M.E. Maron van Rijsbergen (1979: 113 ); Manning Schutze(1999: 538)

Probabilistic Information Retrieval 8 The Probability Ranking Principle “If a reference retrieval system's response to each request is a ranking of the documents in the collection in order of decreasing probability of relevance to the user who submitted the request, where the probabilities are estimated as accurately as possible on the basis of whatever data have been made available to the system for this purpose, the overall effectiveness of the system to its user will be the best that is obtainable on the basis of those data.” ▪ [1960s/1970s] S. Robertson, W.S. Cooper, M.E. Maron; van Rijsbergen (1979:113); Manning & Schütze (1999:538) Probability Ranking Principle

Probabilistic Information Retrieval Probability Ranking Principle Probability ranking principle Let x be a document in the collection Let r represent relevance of a document w r t. a given(fixed) query and let Nr represent non-relevance. R=0, 1)vS NR/R Need to find p(rx)-probability that a document x is relevant. P(RIx)=p(r RP(R) p(R, P(NR)-prior probability p(x) of retrieving a(non) relevant document P(NRLx-p(xINRP(nr p(r x)+p(nr x)= p(xR),p(x NR)-probability that if a relevant(non-relevant) document is retrieved it is x

Probabilistic Information Retrieval 9 Probability Ranking Principle Let x be a document in the collection. Let R represent relevance of a document w.r.t. a given (fixed) query and let NR represent non-relevance. ( ) ( | ) ( ) ( | ) ( ) ( | ) ( ) ( | ) p x p x NR p NR p NR x p x p x R p R p R x = = 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 p(R | x) + p(NR | x) =1 R={0,1} vs. NR/R Probability Ranking Principle

Probabilistic Information Retrieval Probability Ranking Principle Probability ranking principle(Prp Simple case: no selection costs or other utility concerns that would differentially weight errors BayesOptimal Decision Rule x is relevant iff p(rx)>p(nrx PRP in action: Rank all documents by p(rix Theorem Using the prp is optimal, in that it minimizes the loss (Bayes risk under 1/0 loss Provable if all probabilities correct, etc. [e. g ripley 1996]

Probabilistic Information Retrieval 10 Probability Ranking Principle (PRP) ▪ Simple case: no selection costs or other utility concerns that would differentially weight errors ▪ Bayes’ Optimal Decision Rule ▪ x is relevant iff p(R|x) > p(NR|x) ▪ PRP in action: Rank all documents by p(R|x) ▪ Theorem: ▪ Using the PRP is optimal, in that it minimizes the loss (Bayes risk) under 1/0 loss ▪ Provable if all probabilities correct, etc. [e.g., Ripley 1996] Probability Ranking Principle