By Jin Zhang
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Extra info for Visualization for information retrieval
The wide range of applicable similarity methods in a vector space enables people to make an appropriate choice based on a desired similarity measure. By applying different similarity measures to compare a query and a 24 Chapter 2 Information Retrieval Preliminaries document, or a document and a document, people can reveal various properties of compared objects. 5. Information retrieval (evaluation) models such as the distance model, angle model, ellipse model, conjunction model, and disjunction model are available for users to control a search in a vector space.
7 Summary In this chapter, the vector space model and its characteristics have been discussed. Term weighting methods such as stop words, inverse document frequency, the Salton’s term weighting method, probability term weighting method, and so on, were introduced. Similarity measures like the inner product measure, the dice coefficient measure, the Jaccard’s co-efficient measure, the overlap co-efficient measure, the cosine measure, the distance measure, the angle-distance integrated similarity measure, and the Pearson r correlation measure were addressed.
The sum of weights of both vectors as a denominator serves to normalize the inner product measure. The normalization can avoid the problem of unfair calculations as described in the inner product measure. 3 Similarity measures 29 The Jaccard co-efficient measure (or Tanimoto measure) is similar to the Dice coefficient measure in terms of considering features possessed by either of two vectors. It looks similar to the Dice co-efficient measure in terms of normalization. But the methods of normalization are quite different.