EAN: 9783031012006

SYNTHESIS LECTURES ON INFORMATION CONCEPTS RETR...

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-basedmodels. 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. Table of Contents: List of Figures   Preface  
Acknowledgments   Introduction   Foundations of IR Models   Relationships Between IR Models  
Summary & Research Outlook   Bibliography   Author's Biography   Index

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