lecture 2: retrieval models
DESCRIPTION
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. - PowerPoint PPT PresentationTRANSCRIPT
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Lecture 2: Retrieval Models
Maya Ramanath
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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.
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Term weighting (1/3): tf
The lion (Panthera leo) is one of four big cats …
Highly distinctive, the male lion is easily recognised by its mane…
The cat (Felis catus), also known as the domestic cat …
Cats are similar in anatomy to the other felids…
The New World monkeys are classified within the parvorder Platyrrhini, whereas the Old World monkeys form part of the parvorder Catarrhini, which also includes the hominoids…
DOC
COUNT
Lion
Cat
The
D1 2 1 2D2 0 3 2D3 0 0 5D1 D2 D3
Query: Cat
Query: Lion
Query: The Lion
D2 D1 D3
D1 D2 D3
D1 D2 D3
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Term weighting (2/3): tf.idf
The lion (Panthera leo) is one of four big cats …
Highly distinctive, the male lion is easily recognised by its mane…
The cat (Felis catus), also known as the domestic cat …
Cats are similar in anatomy to the other felids…
The New World monkeys are classified within the parvorder Platyrrhini, whereas the Old World monkeys form part of the parvorder Catarrhini, which also includes the hominoids…
DOC
COUNT
Lion
Cat
The
D1 2 1 2D2 0 3 2D3 0 0 5D1 D2 D3
Query: Cat
Query: Lion
Query: The Lion
D2
D1
D3
D1
D2
D3
D1
D2
D3
DOC
WEIGHT
Lion
Cat
The
D1 2/1
1/2
2/3
D2 0 3/2
2/3
D3 0 0 5/3
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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 the cat, a domestic animal”
“Let me tell you about all the animals in the whole world (including the cat) !”
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PROBABILISTIC RANKING
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Binary Independence Model• Rank documents in decreasing
probability of relevance
Derivation is long, but not difficult!
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Let
Let if
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We still need relevant/irrelevant documents
• Sample of corpus, exhaustively judged
• Relevance feedback• Pseudo-relevance feedback
• 2-poisson model• …
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LANGUAGE MODELS
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Intuition (1/2)
Document D Document Q
This is the observationCan we figure out the source?
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Intuition (2/2)PD PQ
Document D Document Q
These are the observations
Can we estimate PD and PQ?
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Query as a sample
Estimated using MLE
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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. ChengXiang Zhai, Foundations and Trends in IR 2(3), 2008
– Also available in the IR book above
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QUESTIONS ?