中度宫颈糜烂症状中度宫颈糜烂患课件.pptVIP

CS276AText Information Retrieval, Mining, and Exploitation Lecture 7 24 Oct 2002 Standard Probabilistic IR IR based on LM One night in a hotel, I saw this late night talk show where Sergey Brin popped on suggesting the web search tip that you should think of some words that would likely appear on pages that would answer your question and use those as your search terms – let’s exploit that idea! Formal Language (Model) Traditional generative model: generates strings Finite state machines or regular grammars, etc. Example: Stochastic Language Models Models probability of generating strings in the language (commonly all strings over ∑) Stochastic Language Models Model probability of generating any string Stochastic Language Models A statistical model for generating text Probability distribution over strings in a given language Unigram and higher-order models Unigram Language Models Bigram (generally, n-gram) Language Models Other Language Models Grammar-based models (PCFGs), etc. Probably not the first thing to try in IR Using Language Models in IR Treat each document as the basis for a model (e.g., unigram sufficient statistics) Rank document d based on P(d | q) P(d | q) = P(q | d) x P(d) / P(q) P(q) is the same for all documents, so ignore P(d) [the prior] is often treated as the same for all d But we could use criteria like authority, length, genre P(q | d) is the probability of q given d’s model Very general formal approach The fundamental problem of LMs Usually we don’t know the model M But have a sample of text representative of that model Estimate a language model from a sample Then compute the observation probability Language Models for IR Language Modeling Approaches Attempt to model query generation process Documents are ranked by the probability that a query would be observed as a random sample from the respective document model Multivariate approach Multinomial approach Retrieval based on probabilistic LM Treat the generation of queries as