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Lecture 2: Retrieval Models

Lecture 2: Retrieval Models. Maya Ramanath. QQ1. Vector space model: 0 for non-presence of a term, 1 for presence: Query: q1 AND q2 AND q3 Compare the set of results returned by the vector space model and boolean model. Term weighting (1/3): tf. Query: Cat. D2. D3. D1. D1. D3.

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Lecture 2: Retrieval Models

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  1. Lecture 2: Retrieval Models Maya Ramanath

  2. QQ1 • Vector space model: 0 for non-presence of a term, 1 for presence: • Query: q1 AND q2 AND q3 Compare the set of results returned by the vector space model and boolean model.

  3. Term weighting (1/3): tf Query: Cat D2 D3 D1 D1 D3 Query: Lion D2 D1 D3 Query: The Lion D2 D3 D2 D1 The lion (Pantheraleo) is one of four big cats … Highly distinctive, the male lion is easily recognised by its mane… The cat (Feliscatus), also known as the domestic cat … Cats are similar in anatomy to the other felids… The New World monkeys are classified within the parvorderPlatyrrhini, whereas the Old World monkeys form part of the parvorderCatarrhini, which also includes the hominoids…

  4. Term weighting (2/3): tf.idf Query: Cat D2 D3 D1 D1 D3 Query: Lion D2 D1 D3 Query: The Lion D2 D3 D2 D1 The lion (Pantheraleo) is one of four big cats … Highly distinctive, the male lion is easily recognised by its mane… The cat (Feliscatus), also known as the domestic cat … Cats are similar in anatomy to the other felids… The New World monkeys are classified within the parvorderPlatyrrhini, whereas the Old World monkeys form part of the parvorderCatarrhini, which also includes the hominoids…

  5. Term weighting (3/3): doc length • Shorter the text, more important the match • Longer the text, more likely you “accidentally” fine a match “Let me tell you about all the animals in the whole world (including the cat) !” “Let me tell you about the cat, a domestic animal”

  6. Probabilistic ranking

  7. Binary Independence Model • Rank documents in decreasing probability of relevance Derivation is long, but not difficult!

  8. Let Let if

  9. We still need relevant/irrelevant documents • Sample of corpus, exhaustively judged • Relevance feedback • Pseudo-relevance feedback • 2-poisson model • …

  10. LANGUage models

  11. Intuition (1/2) Document Q Document D Can we figure out the source? This is the observation

  12. Intuition (2/2) PD • PQ Document D Document Q These are the observations Can we estimate PD and PQ?

  13. Query as a sample Estimated using MLE

  14. References • For term weighting and the long derivation • Introduction to Information Retrieval: Raghavan, Manning and Shuetze, Cambridge University Press, 2008. Also available from: http://nlp.stanford.edu/IR-book/html/htmledition/irbook.html • Language Models • Statistical Language Models for Information Retrieval: A Critical Review. ChengXiangZhai, Foundations and Trends in IR 2(3), 2008 • Also available in the IR book above

  15. QUESTIONS ?

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