1.
Boolean retrieval model
(1)
Posting list intersection:
merging; Query optimization
(2)
Extended Boolean model: term
proximity; Additional information needed: spelling mistake tolerance, compound
or phrases, term frequency, rank(document score)
2.
Vector Space Model
(1)
Parametric and zone indexes: index
and retrieve documents by metadata; simple means for scoring in response to a
query.
(2)
Term frequency and weighting: based
on the statistics of occurrence of the term.
(3)
The vector space model for
scoring: viewing each document as a vector of such weights, we can compute a
score between a query and each document
(4)
Variant tf-idf functions: several
variants of term-weighting
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