core/org.gnit.lucenekmp.search.similarities/Axiomatic

Axiomatic

abstract class Axiomatic(discountOverlaps: Boolean, s: Float, queryLen: Int, k: Float) : SimilarityBase

Axiomatic approaches for IR. From Hui Fang and Chengxiang Zhai 2005. An Exploration of Axiomatic Approaches to Information Retrieval. In Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval (SIGIR '05). ACM, New York, NY, USA, 480-487.

There are a family of models. All of them are based on BM25, Pivoted Document Length Normalization and Language model with Dirichlet prior. Some components (e.g. Term Frequency, Inverted Document Frequency) in the original models are modified so that they follow some axiomatic constraints.

Inheritors

Constructors

constructor(discountOverlaps: Boolean, s: Float, queryLen: Int, k: Float)

constructor(s: Float = 0.25f, queryLen: Int = 1, k: Float = 0.35f)

Constructor setting s and queryLen, letting k to default

Properties

discountOverlaps

val discountOverlaps: Boolean

True if overlap tokens (tokens with a position of increment of zero) are discounted from the document's length.

Functions

open fun computeNorm(state: FieldInvertState): Long

Computes the normalization value for a field at index-time.

open override fun scorer(boost: Float, collectionStats: CollectionStatistics, vararg termStats: TermStatistics): Similarity.SimScorer

Compute any collection-level weight (e.g. IDF, average document length, etc) needed for scoring a query.

abstract override fun toString(): String

Name of the axiomatic method.