Package org.apache.lucene.search.similarities

Class LMJelinekMercerSimilarity

  • public class LMJelinekMercerSimilarity
extends LMSimilarity
    Language model based on the Jelinek-Mercer smoothing method. From Chengxiang Zhai and John Lafferty. 2001. A study of smoothing methods for language models applied to Ad Hoc information retrieval. In Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval (SIGIR '01). ACM, New York, NY, USA, 334-342.

    The model has a single parameter, λ. According to said paper, the optimal value depends on both the collection and the query. The optimal value is around 0.1 for title queries and 0.7 for long queries.

    WARNING: This API is experimental and might change in incompatible ways in the next release.

    • Constructor Detail

      • LMJelinekMercerSimilarity

        public LMJelinekMercerSimilarity​(LMSimilarity.CollectionModel collectionModel,
                                 float lambda)
        Instantiates with the specified collectionModel and λ parameter.

      • LMJelinekMercerSimilarity

        public LMJelinekMercerSimilarity​(float lambda)
        Instantiates with the specified λ parameter.

    • Method Detail

      • score

        protected float score​(BasicStats stats,
                      float freq,
                      float docLen)
        Description copied from class: SimilarityBase
        Scores the document doc.

        Subclasses must apply their scoring formula in this class.

        Specified by:
        score in class SimilarityBase
        Parameters:
        stats - the corpus level statistics.
        freq - the term frequency.
        docLen - the document length.
        Returns:
        the score.

      • explain

        protected void explain​(List<Explanation> subs,
                       BasicStats stats,
                       int doc,
                       float freq,
                       float docLen)
        Description copied from class: SimilarityBase
        Subclasses should implement this method to explain the score. expl already contains the score, the name of the class and the doc id, as well as the term frequency and its explanation; subclasses can add additional clauses to explain details of their scoring formulae.

        The default implementation does nothing.

        Overrides:
        explain in class LMSimilarity
        Parameters:
        subs - the list of details of the explanation to extend
        stats - the corpus level statistics.
        doc - the document id.
        freq - the term frequency.
        docLen - the document length.

      • getLambda

        public float getLambda()
        Returns the λ parameter.