idf weight

  • dft is the document frequency of t: the number of documents that contain t

    • dft is an inverse measure of the informativeness of t

    • dft N

  • We define the idf (inverse document frequency) of t by

    • We use log (N/dft) instead of N/dft to “dampen” the effect of idf.

  • Will turn out the base of the log is immaterial.



idf example, suppose N = 1 million

There is one idf value for each term t in a collection.

idft = log10 = ( N/dft)

  • term
  • dft
  • idft
  • calpurnia
  • 1
  • animal
  • 100
  • sunday
  • 1,000
  • fly
  • 10,000
  • under
  • 100,000
  • the
  • 1,000,000


Effect of idf on ranking

  • Does idf have an effect on ranking for one-term queries, like

    • iPhone

  • idf has no effect on ranking one term queries

    • idf affects the ranking of documents for queries with at least two terms

    • For the query capricious person, idf weighting makes occurrences of capricious count for much more in the final document ranking than occurrences of person.



Collection vs. Document frequency

  • The collection frequency of t is the number of occurrences of t in the collection, counting multiple occurrences.

  • Example:

  • Word

  • Collection frequency

  • Document frequency

  • insurance

  • 10440

  • 3997

  • try

  • 10422

  • 8760

  • Which word is a better search term (and should get a higher weight)?


tf-idf weighting

  • The tf-idf weight of a term is the product of its tf weight and its idf weight.

    Wt,d = log(1+ tft,d) x log10(N/dft)

  • Best known weighting scheme in information retrieval

    • Note: the “-” in tf-idf is a hyphen, not a minus sign!

    • Alternative names: tf.idf, tf x idf

  • Increases with the number of occurrences within a document

  • Increases with the rarity of the term in the collection



Score for a document given a query

Score(q,d) = ∑t∈q∩d tf.idft,d

  • There are many variants

    • How “tf” is computed (with/without logs)

    • Whether the terms in the query are also weighted

    • … 



Binary → count → weight matrix

  • Each document is now represented by a real-valued vector of tf-idf weights ∈ R|V|





Creator: Tgbyrdmc

Contributors:
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