Information retrieval models: foundations and relationships

by Roelleke, Thomas

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Title Statements

Full title :Information retrieval models : foundations and relationships / Thomas Roelleke.

Summary

Information Retrieval (IR) models are a core component of IR research and IR systems. The past decade brought a consolidation of the family of IR models, which by 2000 consisted of relatively isolated views on TF-IDF (Term-Frequency times Inverse-Document-Frequency) as the weighting scheme in the vector-space model (VSM), the probabilistic relevance framework (PRF), the binary independence retrieval (BIR) model, BM25 (Best-Match Version 25, the main instantiation of the PRF/BIR), and language modelling (LM). Also, the early 2000s saw the arrival of divergence from randomness (DFR). Regarding intuition and simplicity, though LM is clear from a probabilistic point of view, several people stated: "It is easy to understand TF-IDF and BM25. For LM, however, we understand the math, but we do not fully understand why it works." This book takes a horizontal approach gathering the foundations of TF-IDF, PRF, BIR, Poisson, BM25, LM, probabilistic inference networks (PIN's), and divergence-based models. The aim is to create a consolidated and balanced view on the main models. A particular focus of this book is on the "relationships between models." This includes an overview over the main frameworks (PRF, logical IR, VSM, generalized VSM) and a pairing of TF-IDF with other models. It becomes evident that TF-IDF and LM measure the same, namely the dependence (overlap) between document and query. The Poisson probability helps to establish probabilistic, non-heuristic roots for TF-IDF, and the Poisson parameter, average term frequency, is a binding link between several retrieval models and model parameters.

Edition Statements

Publication Date:©2013.

Publication Statements

Publisher(s):Springer
Place of Publication:Cham, Switzerland
Language:eng
Publication Date:©2013.

The Physical Object

Description:1 online resource (xxi, 141 pages) : : illustrations

Edition Identifiers

ISBN:9781627050791, 1627050795, 9783031023286, 3031023285
DOI:10.1007/978-3-031-02328-6
oclc:ocn855858906
LC Classification:ZA3075
Dewey Decimal:025.04

1. Introduction -- 1.1 Structure and contribution of this book -- 1.2 Background: a timeline of IR models -- 1.3 Notation -- 1.3.1 The notation issue "term frequency" -- 1.3.2 Notation: Zhai's book and this book.

2. Foundations of IR models -- 2.1 TF-IDF -- 2.1.1 TF variants -- 2.1.2 TFlog: Logarithmic TF -- 2.1.3 TFfrac: fractional (ratio-based) TF -- 2.1.4 IDF variants -- 2.1.5 Term weight and RSV -- 2.1.6 Other TF variants: lifted TF and pivoted TF -- 2.1.7 Semi-subsumed event occurrences: a semantics of the BM25-TF -- 2.1.8 Probabilistic IDF: The probability of being informative -- 2.1.9 Summary -- 2.2 PRF: the probability of relevance framework -- 2.2.1 Feature independence assumption -- 2.2.2 Non-query term assumption -- 2.2.3 Term frequency split -- 2.2.4 Probability ranking principle (PRP) -- 2.2.5 Summary -- 2.3 BIR: binary independence retrieval -- 2.3.1 Term weight and RSV -- 2.3.2 Missing relevance information -- 2.3.3 Variants of the BIR term weight -- 2.3.4 Smooth variants of the BIR term weight -- 2.3.5 RSJ term weight -- 2.3.6 On theoretical arguments for 0.5 in the RSJ term weight -- 2.3.7 Summary -- 2.4 Poisson and 2-Poisson -- 2.4.1 Poisson probability -- 2.4.2…

Subjects

BM25 divergence from randomness (DFR) Foundations & Relationships Information retrieval -- Mathematical m… Information Retrieval (IR) Models LANGUAGE ARTS & DISCIPLINES -- Library … language modelling (LM) POISSON probabilistic roots of IR models probability of relevance framework (PRF) Recherche de l'information -- Modèles m… TF-IDF