IR-part2

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Unformatted text preview:   These are very sparse vectors – most entries are zero §༊  §༊  §༊  §༊  Introduc)on to Informa)on Retrieval Sec. 6.3 Queries as vectors §༊  Key idea 1: Do the same for queries: represent them as vectors in the space §༊  Key idea 2: Rank documents according to their proximity to the query in this space §༊  proximity = similarity of vectors §༊  proximity ≈ inverse of distance §༊  Recall: We do this because we want to get away from the you’re- either- in- or- out Boolean model §༊  Instead: rank more relevant documents higher than less relevant documents Introduc)on to Informa)on Retrieval Sec. 6.3 Formalizing vector space proximity §༊  First cut: distance between two points §༊  ( = distance between the end points of the two vectors) §...
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