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CS276A Information Retrieval Lecture 6 Plan Last lecture Index construction This lecture Parametric and field searches Zones in documents Scoring documents: zone ...
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CS276A Information Retrieval Lecture 9 Recap of the last lecture Results summaries Evaluating a search engine Benchmarks Precision and recall Example 11pt precision ...
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CS276A Text Retrieval and Mining Lecture 11 Recap of the last lecture Probabilistic models in Information Retrieval Probability Ranking Principle Binary Independence ...
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CS276A Text Retrieval and Mining Lecture 10 Recap of the last lecture Improving search results Especially for high recall. E.g., searching for aircraft so it matches ...
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... Today The Language Model Approach to IR Basic query generation ... Model) Traditional generative model: ... motivated probabilistic model of ...
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Title: CS276B Text Information Retrieval, Mining, and Exploitation Author: Christopher Manning Last modified by: Christopher Manning Created Date
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How big is the lexicon V? Grows (but more slowly) with corpus size. Empirically okay model: ... Query car tyres car tyres automobile tires. Can expand index ...
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CS276A Text Retrieval and Mining Lecture 13 [Borrows s from Ray Mooney and Soumen Chakrabarti] Recap: The Language Model Approach to IR Consider probability of ...
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CS276A Text Information Retrieval, Mining, and Exploitation Supplemental Min-wise Hashing Slides [Brod97,Brod98] (Adapted from Rajeev Motwani s CS361A s)
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... basics for the project Possible project topics Helpful tools you might want to know about Overview of 276B Consider it the ... Project presentations ...
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measure of informativeness of term: its rarity across the whole corpus ... The standard measure in the TREC competitions: you take the precision at 11 ...
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e.g., 'editorials' 'movie-reviews' 'news' Labels may be opinion ... rec.autos: car, cars, engine, ford, dealer, mustang, oil, collision, autos, tires, toyota, ...
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Benchmarks. Precision and recall. Results summaries. Summaries ... Standard relevance benchmarks ... Reuters and other benchmark doc collections used 'Retrieval ...
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Mars surface images. Nikon CoolPix. Car rental Finland ... First generation: using link counts as simple measures of popularity. Two basic suggestions: ...
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Tadpole. Search engine spam. Lexical chains. English text compression. Recommendation systems ... Tadpole. Mahabhashyam and Singitham, Fall 2002 ...
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.com / .org / .net / international urls. cnn.com vs. www.cnn.com ... TREC-6 interactive TREC report. 9 participating groups (US, Europe, Australia) ...
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Maximizes the distance between the hyperplane and the 'difficult points' close ... Examples closest to the hyperplane are support vectors. ...
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user satisfaction ratings. correlation or mean squared error (if you're predicting values) ... predicted ratings are weighted averages using user's Pearson correlation ...
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Title: CS276A Text Information Retrieval, Mining, and Exploitation Author: Christopher Manning Last modified by: admin Created Date: 10/22/2002 6:34:39 AM
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Title: CS276A Text Information Retrieval, Mining, and Exploitation Author: Christopher Manning Last modified by: tyang Created Date: 10/20/2002 8:34:30 AM
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CS276A. Text Information Retrieval, Mining, and Exploitation. Lecture ... Ferrari Mondial. Ferrari Mondial. Ferrari mondial. ferrari mondial 'Ferrari Mondial' ...
The web and ist challenges. How do we find information in the WWW? Search Engines, Crawling, Indexing, Ranking. How does the web ... QUILT. Resources. The Web ...
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How do I describe and exchange data? XML as Data Exchange Language. How do I call procedures over the Web? ... How do I include meaning in the WWW? Semantic Web ...
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Imagine a surfer surfing the WWW. At each step of the walk, the surfer will perform ... Let xp(t) be the probability that the surfer is at the page p at time t. ...
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Random projection theorem: http://citeseer.nj.nec.com/dasgupta99elementary.html. Faster random projection: http://citeseer.nj.nec.com/frieze98fast.html ...
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Simple, expensive at test time, high variance, non-linear. Vector space classification using centroids and ... TOPICS D livestock /D D hog /D /TOPICS ...
