Class Js_KLs

  • All Implemented Interfaces:, java.lang.Cloneable, Model

    public class Js_KLs
    extends WeightingModel
    This class implements the Js_KLs weighting model, which is the product of two measures: the Jefrreys' divergence with the Kullback Leibler's divergence. The two measures are obtained by the addition of one query token. Then Jefrreys' divergence and the information growth in the document by Kullback Leibler's divergence are computed. The model computes the product of these two information measures as amount of information carried by a single query token. Js_KLs is an unsupervised model (parameter free model) of IR.

    Js_KLs has a high performance but it can be used with verbose queries. In particular, it has statistically or moderately significant better MAP performance than most of the supervised models with long queries on the terabyte collection (GOV2) with the exception of PL2. MAP for long topics, and comparative p values (two-tailed paired t-test) compared to supervised models (with optimal MAP parameter values) are as follows:

    QueriesMAP of JS_KLsLGDDirichlet_LMPL2BM25In_expB2
    long0.3178 (>) p=1.7E-17(>) p=0.0544(<) p=0.3155(>) p=0.7866(>) p=5151


    1. Frequentist and Bayesian approach to Information Retrieval. G. Amati. In Proceedings of the 28th European Conference on IR Research (ECIR 2006). LNCS vol 3936, pages 13--24.
    Gianni Amati
    See Also:
    Serialized Form
    • Constructor Detail

      • Js_KLs

        public Js_KLs()
        A default constructor to make this model.
    • Method Detail

      • getInfo

        public final java.lang.String getInfo()
        Returns the name of the model, in this case "Js_KLs"
        Specified by:
        getInfo in interface Model
        Specified by:
        getInfo in class WeightingModel
        the name of the model
      • score

        public final double score​(double tf,
                                  double docLength)
        Uses Js_KLs to compute a weight for a term in a document.
        Specified by:
        score in class WeightingModel
        tf - The term frequency of the term in the document
        docLength - the document's length
        the score assigned to a document with the given tf and docLength, and other preset parameters